Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach

PubMed Central

Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian

2015-01-01

Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks. PMID:26729123

Spatio-Temporal Constrained Human Trajectory Generation from the PIR Motion Detector Sensor Network Data: A Geometric Algebra Approach.

PubMed

Yu, Zhaoyuan; Yuan, Linwang; Luo, Wen; Feng, Linyao; Lv, Guonian

2015-12-30

Passive infrared (PIR) motion detectors, which can support long-term continuous observation, are widely used for human motion analysis. Extracting all possible trajectories from the PIR sensor networks is important. Because the PIR sensor does not log location and individual information, none of the existing methods can generate all possible human motion trajectories that satisfy various spatio-temporal constraints from the sensor activation log data. In this paper, a geometric algebra (GA)-based approach is developed to generate all possible human trajectories from the PIR sensor network data. Firstly, the representation of the geographical network, sensor activation response sequences and the human motion are represented as algebraic elements using GA. The human motion status of each sensor activation are labeled using the GA-based trajectory tracking. Then, a matrix multiplication approach is developed to dynamically generate the human trajectories according to the sensor activation log and the spatio-temporal constraints. The method is tested with the MERL motion database. Experiments show that our method can flexibly extract the major statistical pattern of the human motion. Compared with direct statistical analysis and tracklet graph method, our method can effectively extract all possible trajectories of the human motion, which makes it more accurate. Our method is also likely to provides a new way to filter other passive sensor log data in sensor networks.

The impact of poverty on the development of brain networks

PubMed Central

Lipina, Sebastián J.; Posner, Michael I.

2012-01-01

Although the study of brain development in non-human animals is an old one, recent imaging methods have allowed non-invasive studies of the gray and white matter of the human brain over the lifespan. Classic animal studies show clearly that impoverished environments reduce cortical gray matter in relation to complex environments and cognitive and imaging studies in humans suggest which networks may be most influenced by poverty. Studies have been clear in showing the plasticity of many brain systems, but whether sensitivity to learning differs over the lifespan and for which networks is still unclear. A major task for current research is a successful integration of these methods to understand how development and learning shape the neural networks underlying achievements in literacy, numeracy, and attention. This paper seeks to foster further integration by reviewing the current state of knowledge relating brain changes to behavior and indicating possible future directions. PMID:22912613

Undergraduate students' development of social, cultural, and human capital in a networked research experience

NASA Astrophysics Data System (ADS)

Thompson, Jennifer Jo; Conaway, Evan; Dolan, Erin L.

2016-12-01

Recent calls for reform in undergraduate biology education have emphasized integrating research experiences into the learning experiences of all undergraduates. Contemporary science research increasingly demands collaboration across disciplines and institutions to investigate complex research questions, providing new contexts and models for involving undergraduates in research. In this study, we examined the experiences of undergraduates participating in a multi-institution and interdisciplinary biology research network. Unlike the traditional apprenticeship model of research, in which a student participates in research under the guidance of a single faculty member, students participating in networked research have the opportunity to develop relationships with additional faculty and students working in other areas of the project, at their own and at other institutions. We examined how students in this network develop social ties and to what extent a networked research experience affords opportunities for students to develop social, cultural, and human capital. Most studies of undergraduate involvement in science research have focused on documenting student outcomes rather than elucidating how students gain access to research experiences or how elements of research participation lead to desired student outcomes. By taking a qualitative approach framed by capital theories, we have identified ways that undergraduates utilize and further develop various forms of capital important for success in science research. In our study of the first 16 months of a biology research network, we found that undergraduates drew upon a combination of human, cultural, and social capital to gain access to the network. Within their immediate research groups, students built multidimensional social ties with faculty, peers, and others, yielding social capital that can be drawn upon for information, resources, and support. They reported developing cultural capital in the form of learning to think and work like a scientist—a scientific habitus. They reported developing human capital in the forms of technical, analytical, and communication skills in scientific research. Most of the students had little, direct interaction with network members in other research groups and thus developed little cross-institutional capital. The exception to this trend was at one institution that housed three research groups. Because proximity facilitated shared activities, students across research groups at this institution developed cross-lab ties with faculty and peers through which they developed social, cultural, and human capital. An important long-term concern is whether the capital students have developed will help them access opportunities in science beyond the network. At this point, many undergraduates have had limited opportunities to actually draw on capital beyond the network. Nevertheless, a number of students demonstrated awareness that they had developed resources that they could use in other scientific contexts.

Network-Based Leadership Development: A Guiding Framework and Resources for Management Educators

ERIC Educational Resources Information Center

Cullen-Lester, Kristin L.; Woehler, Meredith L.; Willburn, Phil

2016-01-01

Management education and leadership development has traditionally focused on improving human capital (i.e., knowledge, skills, and abilities). Social capital, networks, and networking skills have received less attention. When this content has been incorporated into learning and development experiences, it has often been more ad hoc and has…

Approaching human language with complex networks.

PubMed

Cong, Jin; Liu, Haitao

2014-12-01

The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics). Copyright © 2014 Elsevier B.V. All rights reserved.

Infant joint attention, neural networks and social cognition.

PubMed

Mundy, Peter; Jarrold, William

2010-01-01

Neural network models of attention can provide a unifying approach to the study of human cognitive and emotional development (Posner & Rothbart, 2007). In this paper we argue that a neural network approach to the infant development of joint attention can inform our understanding of the nature of human social learning, symbolic thought process and social cognition. At its most basic, joint attention involves the capacity to coordinate one's own visual attention with that of another person. We propose that joint attention development involves increments in the capacity to engage in simultaneous or parallel processing of information about one's own attention and the attention of other people. Infant practice with joint attention is both a consequence and an organizer of the development of a distributed and integrated brain network involving frontal and parietal cortical systems. This executive distributed network first serves to regulate the capacity of infants to respond to and direct the overt behavior of other people in order to share experience with others through the social coordination of visual attention. In this paper we describe this parallel and distributed neural network model of joint attention development and discuss two hypotheses that stem from this model. One is that activation of this distributed network during coordinated attention enhances the depth of information processing and encoding beginning in the first year of life. We also propose that with development, joint attention becomes internalized as the capacity to socially coordinate mental attention to internal representations. As this occurs the executive joint attention network makes vital contributions to the development of human symbolic thinking and social cognition. Copyright © 2010 Elsevier Ltd. All rights reserved.

Neural Network Development Tool (NETS)

NASA Technical Reports Server (NTRS)

Baffes, Paul T.

1990-01-01

Artificial neural networks formed from hundreds or thousands of simulated neurons, connected in manner similar to that in human brain. Such network models learning behavior. Using NETS involves translating problem to be solved into input/output pairs, designing network configuration, and training network. Written in C.

The fierce ox becomes tame on strange ground

NASA Astrophysics Data System (ADS)

Ertsen, Maurits

2015-04-01

Obviously, the science of change in hydrology and society requires the humanities and social sciences to be an integrated part of the academic project. With the promise that serious study might actually change the hydrological framework, human-environmental interactions need to be studied. An issue is obviously how to ensure that social action by human agents is well represented. In this paper, I propose to rethink human's role within its own environment: people create environments expecting specific results (even though people have their own agendas). The group of 'people' is not a homogenous category. Bruno Latour, the French sociologist and philosopher, argues that human decision making and development of societal institutions is a local activity, constructed within networks of actors. Networks are continuously created and recreated by human actors engaging with other human actors and non-human intermediaries. The resulting networks build links between short and long term human responses - from individuals to societies - in terms of actions, policies, interventions and the like in relation to the - often stochastic - nature of water flows and systems on different scales. Through Actor Network Theory, I suggest new insights from this approach as well as the pitfalls to understand how networks of people and material conditions shape policy development for and management of water resources. I discuss how a focus on the short-term, small-scale interactions among people, their environments and technology in developing water policies and managing water systems can yield insights relevant for those involved in current water policy. How representatives of society itself can be part of this remains an issue. The paper will suggest some ideas from a project setup in the Netherlands, in which researchers, water managers and heritage agencies collaborate.

77 FR 14025 - Eunice Kennedy Shriver National Institute of Child Health & Human Development; Notice of Closed...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-08

... Institute of Child Health and Human Development Special Emphasis Panel; Autism Center of Excellence: Network... National Institute of Child Health & Human Development; Notice of Closed Meeting Pursuant to section 10(d... Kennedy Shriver National Institute of Child Health and Human Development, NIH, 6100 executive blvd., Room...

Developmental implications of children's brain networks and learning.

PubMed

Chan, John S Y; Wang, Yifeng; Yan, Jin H; Chen, Huafu

2016-10-01

The human brain works as a synergistic system where information exchanges between functional neuronal networks. Rudimentary networks are observed in the brain during infancy. In recent years, the question of how functional networks develop and mature in children has been a hotly discussed topic. In this review, we examined the developmental characteristics of functional networks and the impacts of skill training on children's brains. We first focused on the general rules of brain network development and on the typical and atypical development of children's brain networks. After that, we highlighted the essentials of neural plasticity and the effects of learning on brain network development. We also discussed two important theoretical and practical concerns in brain network training. Finally, we concluded by presenting the significance of network training in typically and atypically developed brains.

Amplifying human ability through autonomics and machine learning in IMPACT

NASA Astrophysics Data System (ADS)

Dzieciuch, Iryna; Reeder, John; Gutzwiller, Robert; Gustafson, Eric; Coronado, Braulio; Martinez, Luis; Croft, Bryan; Lange, Douglas S.

2017-05-01

Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.

77 FR 66076 - Eunice Kennedy Shriver National Institute of Child Health & Human Development; Notice of Closed...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-01

... DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health Eunice Kennedy Shriver National Institute of Child Health & Human Development; Notice of Closed Meeting Pursuant to section 10(d...: National Institute of Child Health and Human Development Special Emphasis Panel; Neonatal Research Network...

75 FR 34462 - Eunice Kennedy Shriver National Institute of Child Health and Human Development; Notice of Closed...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-17

... Institute of Child Health and Human Development Special Emphasis Panel; Adolescent Medicine Trials Network... National Institute of Child Health and Human Development; Notice of Closed Meeting Pursuant to section 10(d..., National Institute of Child Health and Human Development, NIH, 6100 Executive Blvd. Room 5b01, Bethesda, MD...

Development and application of deep convolutional neural network in target detection

NASA Astrophysics Data System (ADS)

Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

2018-04-01

With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

The HOX genes are expressed, in vivo, in human tooth germs: in vitro cAMP exposure of dental pulp cells results in parallel HOX network activation and neuronal differentiation.

PubMed

D'Antò, Vincenzo; Cantile, Monica; D'Armiento, Maria; Schiavo, Giulia; Spagnuolo, Gianrico; Terracciano, Luigi; Vecchione, Raffaela; Cillo, Clemente

2006-03-01

Homeobox-containing genes play a crucial role in odontogenesis. After the detection of Dlx and Msx genes in overlapping domains along maxillary and mandibular processes, a homeobox odontogenic code has been proposed to explain the interaction between different homeobox genes during dental lamina patterning. No role has so far been assigned to the Hox gene network in the homeobox odontogenic code due to studies on specific Hox genes and evolutionary considerations. Despite its involvement in early patterning during embryonal development, the HOX gene network, the most repeat-poor regions of the human genome, controls the phenotype identity of adult eukaryotic cells. Here, according to our results, the HOX gene network appears to be active in human tooth germs between 18 and 24 weeks of development. The immunohistochemical localization of specific HOX proteins mostly concerns the epithelial tooth germ compartment. Furthermore, only a few genes of the network are active in embryonal retromolar tissues, as well as in ectomesenchymal dental pulp cells (DPC) grown in vitro from adult human molar. Exposure of DPCs to cAMP induces the expression of from three to nine total HOX genes of the network in parallel with phenotype modifications with traits of neuronal differentiation. Our observations suggest that: (i) by combining its component genes, the HOX gene network determines the phenotype identity of epithelial and ectomesenchymal cells interacting in the generation of human tooth germ; (ii) cAMP treatment activates the HOX network and induces, in parallel, a neuronal-like phenotype in human primary ectomesenchymal dental pulp cells. 2005 Wiley-Liss, Inc.

Beyond dark and bright: towards a more holistic understanding of inter-group networks.

PubMed

Hejnova, Petra

2010-01-01

Networks are becoming a popular organizational form for structuring human activities. To date, scholars have addressed networks in a variety of fields, including sociology, economics, public administration, criminology, political science, and international security. However, little has been done so far to systematically examine the similarities, differences, and connections between network forms of organization across different academic disciplines. This has important implications for both theory and practice. The lack of attention paid to organizational similarities and differences prevents the exchange of knowledge developed across fields. In turn, policy-makers cannot take full advantage of existing research, and may miss opportunities to improve the work of some networks and combat that of others. To address this gap in the literature, this paper uses the combination of organizational environments and organizational goals to develop a new typology of inter-group networks, and thus improve our understanding of how human behaviour is coordinated through networks.

Collaborative research networks in health: a pragmatic scoping study for the development of an imaging network.

PubMed

Robinson, Tracy Elizabeth; Rankin, Nicole; Janssen, Anna; Mcgregor, Deborah; Grieve, Stuart; Shaw, Timothy

2015-12-09

Collaborative research networks are often touted as a solution for enhancing the translation of knowledge, but questions remain about how to evaluate their impact on health service delivery. This pragmatic scoping study explored the enabling factors for developing and supporting a collaborative imaging network in a metropolitan university in Australia. An advisory group was established to provide governance and to identify key informants and participants. Focus group discussions (n = 2) and semi-structured interviews (n = 22) were facilitated with representatives from a broad range of disciplines. In addition, a survey, a review of relevant websites (n = 15) and a broad review of the literature were undertaken to elicit information on collaborative research networks and perceived needs and factors that would support their involvement in a multi-disciplinary collaborative research network. Findings were de-identified and broad themes were identified. Participants identified human factors as having priority for developing and sustaining a collaborative research network. In particular, leadership, a shared vision and a communication plan that includes social media were identified as crucial for sustaining an imaging network in health research. It is important to develop metrics that map relationships between network members and the role that communication tools can contribute to this process. This study confirms that human factors remain significant across a range of collaborative endeavours. The use of focus group discussions, interviews, and literature and website reviews means we can now strongly recommend the primacy of human factors. More work is needed to identify how the network operates and what specific indicators or metrics help build the capacity of clinicians and scientists to participate in translational research.

The maturation of cortical sleep rhythms and networks over early development.

PubMed

Chu, C J; Leahy, J; Pathmanathan, J; Kramer, M A; Cash, S S

2014-07-01

Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

The maturation of cortical sleep rhythms and networks over early development

PubMed Central

Chu, CJ; Leahy, J; Pathmanathan, J; Kramer, MA; Cash, SS

2014-01-01

Objective Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. Methods We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. Results We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. Conclusion Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. Significance This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development. PMID:24418219

A vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability

PubMed Central

2014-01-01

Background Numerous inflammation-related pathways have been shown to play important roles in atherogenesis. Rapid and efficient assessment of the relative influence of each of those pathways is a challenge in the era of “omics” data generation. The aim of the present work was to develop a network model of inflammation-related molecular pathways underlying vascular disease to assess the degree of translatability of preclinical molecular data to the human clinical setting. Methods We constructed and evaluated the Vascular Inflammatory Processes Network (V-IPN), a model representing a collection of vascular processes modulated by inflammatory stimuli that lead to the development of atherosclerosis. Results Utilizing the V-IPN as a platform for biological discovery, we have identified key vascular processes and mechanisms captured by gene expression profiling data from four independent datasets from human endothelial cells (ECs) and human and murine intact vessels. Primary ECs in culture from multiple donors revealed a richer mapping of mechanisms identified by the V-IPN compared to an immortalized EC line. Furthermore, an evaluation of gene expression datasets from aortas of old ApoE-/- mice (78 weeks) and human coronary arteries with advanced atherosclerotic lesions identified significant commonalities in the two species, as well as several mechanisms specific to human arteries that are consistent with the development of unstable atherosclerotic plaques. Conclusions We have generated a new biological network model of atherogenic processes that demonstrates the power of network analysis to advance integrative, systems biology-based knowledge of cross-species translatability, plaque development and potential mechanisms leading to plaque instability. PMID:24965703

RELAGH - The challenge of having a scientific network in Latin America: An account from the presidents.

PubMed

Rojas-Martínez, Augusto; Giraldo-Ríos, Alejandro; Jiménez-Arce, Gerardo; de Vargas, Aída Falcón; Giugliani, Roberto

2014-03-01

Latin America and the Caribbean region make up one of the largest areas of the world, and this region is characterized by a complex mixture of ethnic groups sharing Iberian languages. The area is comprised of nations and regions with different levels of social development. This region has experienced historical advances in the last decades to increase the minimal standards of quality of life; however, several factors, such as concentrated populations in large urban centers and isolated and poor communities, still have an important impact on medical services, particularly genetics services. Latin American researchers have greatly contributed to the development of human genetics and historic inter-ethnic diversity, and the multiplicity of geographic areas are unique for the study of gene-environment interactions. As a result of regional developments in the fields of human and medical genetics, the Latin American Network of Human Genetics (Red Latinoamericana de Genética Humana - RELAGH) was created in 2001 to foster the networking of national associations and societies dedicated to these scientific disciplines. RELAGH has developed important educational activities, such as the Latin American School of Human and Medical Genetics (ELAG), and has held three biannual meetings to encourage international research cooperation among the member countries and international organizations. Since its foundation, RELAGH has been admitted as a full regional member to the International Federation of Human Genetics Societies. This article describes the historical aspects, activities, developments, and challenges that are still faced by the Network.

RELAGH - The challenge of having a scientific network in Latin America: An account from the presidents

PubMed Central

Rojas-Martínez, Augusto; Giraldo-Ríos, Alejandro; Jiménez-Arce, Gerardo; de Vargas, Aída Falcón; Giugliani, Roberto

2014-01-01

Latin America and the Caribbean region make up one of the largest areas of the world, and this region is characterized by a complex mixture of ethnic groups sharing Iberian languages. The area is comprised of nations and regions with different levels of social development. This region has experienced historical advances in the last decades to increase the minimal standards of quality of life; however, several factors, such as concentrated populations in large urban centers and isolated and poor communities, still have an important impact on medical services, particularly genetics services. Latin American researchers have greatly contributed to the development of human genetics and historic inter-ethnic diversity, and the multiplicity of geographic areas are unique for the study of gene-environment interactions. As a result of regional developments in the fields of human and medical genetics, the Latin American Network of Human Genetics (Red Latinoamericana de Genética Humana - RELAGH) was created in 2001 to foster the networking of national associations and societies dedicated to these scientific disciplines. RELAGH has developed important educational activities, such as the Latin American School of Human and Medical Genetics (ELAG), and has held three biannual meetings to encourage international research cooperation among the member countries and international organizations. Since its foundation, RELAGH has been admitted as a full regional member to the International Federation of Human Genetics Societies. This article describes the historical aspects, activities, developments, and challenges that are still faced by the Network. PMID:24764765

Reverse Engineering Validation using a Benchmark Synthetic Gene Circuit in Human Cells

PubMed Central

Kang, Taek; White, Jacob T.; Xie, Zhen; Benenson, Yaakov; Sontag, Eduardo; Bleris, Leonidas

2013-01-01

Multi-component biological networks are often understood incompletely, in large part due to the lack of reliable and robust methodologies for network reverse engineering and characterization. As a consequence, developing automated and rigorously validated methodologies for unraveling the complexity of biomolecular networks in human cells remains a central challenge to life scientists and engineers. Today, when it comes to experimental and analytical requirements, there exists a great deal of diversity in reverse engineering methods, which renders the independent validation and comparison of their predictive capabilities difficult. In this work we introduce an experimental platform customized for the development and verification of reverse engineering and pathway characterization algorithms in mammalian cells. Specifically, we stably integrate a synthetic gene network in human kidney cells and use it as a benchmark for validating reverse engineering methodologies. The network, which is orthogonal to endogenous cellular signaling, contains a small set of regulatory interactions that can be used to quantify the reconstruction performance. By performing successive perturbations to each modular component of the network and comparing protein and RNA measurements, we study the conditions under which we can reliably reconstruct the causal relationships of the integrated synthetic network. PMID:23654266

Reverse engineering validation using a benchmark synthetic gene circuit in human cells.

PubMed

Kang, Taek; White, Jacob T; Xie, Zhen; Benenson, Yaakov; Sontag, Eduardo; Bleris, Leonidas

2013-05-17

Multicomponent biological networks are often understood incompletely, in large part due to the lack of reliable and robust methodologies for network reverse engineering and characterization. As a consequence, developing automated and rigorously validated methodologies for unraveling the complexity of biomolecular networks in human cells remains a central challenge to life scientists and engineers. Today, when it comes to experimental and analytical requirements, there exists a great deal of diversity in reverse engineering methods, which renders the independent validation and comparison of their predictive capabilities difficult. In this work we introduce an experimental platform customized for the development and verification of reverse engineering and pathway characterization algorithms in mammalian cells. Specifically, we stably integrate a synthetic gene network in human kidney cells and use it as a benchmark for validating reverse engineering methodologies. The network, which is orthogonal to endogenous cellular signaling, contains a small set of regulatory interactions that can be used to quantify the reconstruction performance. By performing successive perturbations to each modular component of the network and comparing protein and RNA measurements, we study the conditions under which we can reliably reconstruct the causal relationships of the integrated synthetic network.

New strategy for drug discovery by large-scale association analysis of molecular networks of different species.

PubMed

Zhang, Bo; Fu, Yingxue; Huang, Chao; Zheng, Chunli; Wu, Ziyin; Zhang, Wenjuan; Yang, Xiaoyan; Gong, Fukai; Li, Yuerong; Chen, Xiaoyu; Gao, Shuo; Chen, Xuetong; Li, Yan; Lu, Aiping; Wang, Yonghua

2016-02-25

The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development.

Toward Developmental Connectomics of the Human Brain

PubMed Central

Cao, Miao; Huang, Hao; Peng, Yun; Dong, Qi; He, Yong

2016-01-01

Imaging connectomics based on graph theory has become an effective and unique methodological framework for studying structural and functional connectivity patterns of the developing brain. Normal brain development is characterized by continuous and significant network evolution throughout infancy, childhood, and adolescence, following specific maturational patterns. Disruption of these normal changes is associated with neuropsychiatric developmental disorders, such as autism spectrum disorders or attention-deficit hyperactivity disorder. In this review, we focused on the recent progresses regarding typical and atypical development of human brain networks from birth to early adulthood, using a connectomic approach. Specifically, by the time of birth, structural networks already exhibit adult-like organization, with global efficient small-world and modular structures, as well as hub regions and rich-clubs acting as communication backbones. During development, the structure networks are fine-tuned, with increased global integration and robustness and decreased local segregation, as well as the strengthening of the hubs. In parallel, functional networks undergo more dramatic changes during maturation, with both increased integration and segregation during development, as brain hubs shift from primary regions to high order functioning regions, and the organization of modules transitions from a local anatomical emphasis to a more distributed architecture. These findings suggest that structural networks develop earlier than functional networks; meanwhile functional networks demonstrate more dramatic maturational changes with the evolution of structural networks serving as the anatomical backbone. In this review, we also highlighted topologically disorganized characteristics in structural and functional brain networks in several major developmental neuropsychiatric disorders (e.g., autism spectrum disorders, attention-deficit hyperactivity disorder and developmental dyslexia). Collectively, we showed that delineation of the brain network from a connectomics perspective offers a unique and refreshing view of both normal development and neuropsychiatric disorders. PMID:27064378

Development of human locomotion.

PubMed

Lacquaniti, Francesco; Ivanenko, Yuri P; Zago, Myrka

2012-10-01

Neural control of locomotion in human adults involves the generation of a small set of basic patterned commands directed to the leg muscles. The commands are generated sequentially in time during each step by neural networks located in the spinal cord, called Central Pattern Generators. This review outlines recent advances in understanding how motor commands are expressed at different stages of human development. Similar commands are found in several other vertebrates, indicating that locomotion development follows common principles of organization of the control networks. Movements show a high degree of flexibility at all stages of development, which is instrumental for learning and exploration of variable interactions with the environment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Efficient prediction of human protein-protein interactions at a global scale.

PubMed

Schoenrock, Andrew; Samanfar, Bahram; Pitre, Sylvain; Hooshyar, Mohsen; Jin, Ke; Phillips, Charles A; Wang, Hui; Phanse, Sadhna; Omidi, Katayoun; Gui, Yuan; Alamgir, Md; Wong, Alex; Barrenäs, Fredrik; Babu, Mohan; Benson, Mikael; Langston, Michael A; Green, James R; Dehne, Frank; Golshani, Ashkan

2014-12-10

Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

77 FR 62243 - Rural Health Network Development Program

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-12

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Health Resources and Services Administration Rural Health Network Development Program AGENCY: Health Resources and Services Administration (HRSA), HHS. ACTION: Notice of Non-competitive Replacement Award to Siloam Springs Regional Health Cooperative, Inc. SUMMARY...

Resolving Structural Variability in Network Models and the Brain

PubMed Central

Klimm, Florian; Bassett, Danielle S.; Carlson, Jean M.; Mucha, Peter J.

2014-01-01

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data. PMID:24675546

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Shensa, Mark J.; Remington, Roger W. (Technical Monitor)

1998-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

NASA Technical Reports Server (NTRS)

Trejo, L. J.; Shensa, M. J.

1999-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

BrainNet Viewer: a network visualization tool for human brain connectomics.

PubMed

Xia, Mingrui; Wang, Jinhui; He, Yong

2013-01-01

The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

Small-world human brain networks: Perspectives and challenges.

PubMed

Liao, Xuhong; Vasilakos, Athanasios V; He, Yong

2017-06-01

Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field. Copyright © 2017 Elsevier Ltd. All rights reserved.

Produsage in hybrid networks: sociotechnical skills in the case of Arduino

NASA Astrophysics Data System (ADS)

De Paoli, Stefano; Storni, Cristiano

2011-04-01

In 1this paper we investigate produsage using Actor-Network Theory with a focus on (produsage) skills, their development, and transformation. We argue that produsage is not a model that determines a change in the traditional consumption/production paradigm through a series of essential preconditions (such as open participation, peer-sharing, or common ownership). Rather, we explain produsage as the open-ended result of a series of heterogeneous actor-networking strategies. In this view, the so-called preconditions do not explain produsage but have to be explained along with its establishment as an actor-network. Drawing on this approach, we discuss a case study of an open hardware project: the Arduino board, and we develop a perspective that maps the skills of human and non-human entities in produsage actor-networks, showing how skills are symmetrical, relational, and circulating.

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions

PubMed Central

Box, Simon

2014-01-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human ‘player’ to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable. PMID:26064570

Supervised learning from human performance at the computationally hard problem of optimal traffic signal control on a network of junctions.

PubMed

Box, Simon

2014-12-01

Optimal switching of traffic lights on a network of junctions is a computationally intractable problem. In this research, road traffic networks containing signallized junctions are simulated. A computer game interface is used to enable a human 'player' to control the traffic light settings on the junctions within the simulation. A supervised learning approach, based on simple neural network classifiers can be used to capture human player's strategies in the game and thus develop a human-trained machine control (HuTMaC) system that approaches human levels of performance. Experiments conducted within the simulation compare the performance of HuTMaC to two well-established traffic-responsive control systems that are widely deployed in the developed world and also to a temporal difference learning-based control method. In all experiments, HuTMaC outperforms the other control methods in terms of average delay and variance over delay. The conclusion is that these results add weight to the suggestion that HuTMaC may be a viable alternative, or supplemental method, to approximate optimization for some practical engineering control problems where the optimal strategy is computationally intractable.

Educating the Human Brain. Human Brain Development Series

ERIC Educational Resources Information Center

Posner, Michael I.; Rothbart, Mary K.

2006-01-01

"Educating the Human Brain" is the product of a quarter century of research. This book provides an empirical account of the early development of attention and self regulation in infants and young children. It examines the brain areas involved in regulatory networks, their connectivity, and how their development is influenced by genes and…

Molecular networks and the evolution of human cognitive specializations.

PubMed

Fontenot, Miles; Konopka, Genevieve

2014-12-01

Inroads into elucidating the origins of human cognitive specializations have taken many forms, including genetic, genomic, anatomical, and behavioral assays that typically compare humans to non-human primates. While the integration of all of these approaches is essential for ultimately understanding human cognition, here, we review the usefulness of coexpression network analysis for specifically addressing this question. An increasing number of studies have incorporated coexpression networks into brain expression studies comparing species, disease versus control tissue, brain regions, or developmental time periods. A clearer picture has emerged of the key genes driving brain evolution, as well as the developmental and regional contributions of gene expression patterns important for normal brain development and those misregulated in cognitive diseases. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multilayer limb quasi-static electromagnetic modeling with experiments for Galvanic coupling type IBC.

PubMed

Pun, S H; Gao, Y M; Mou, P A; Mak, P U; Vai, M I; Du, M

2010-01-01

Intra-body communication (IBC) is a new, emerging, short-range and human body based communication methodology. It is a technique to network various devices on human body, by utilizing the conducting properties of human tissues. For currently fast developed Body area network(BAN)/Body sensor network(BSN), IBC is believed to have advantages in power consumption, electromagnetic radiation, interference from external electromagnetic noise, security, and restriction in spectrum resource. In this article, the authors propose an improved mathematical model, which includes both electrical properties and proportion of human tissues, for IBC on a human limb. By solving the mathematical model analytically on four-layer system (skin, fat, muscle, and bone) and conducting in-vivo experiment, a comparison has been conducted.

Visual social network analysis: effective approach to model complex human social, behaviour & culture.

PubMed

Ahram, Tareq Z; Karwowski, Waldemar

2012-01-01

The advent and adoption of internet-based social networking has significantly altered our daily lives. The educational community has taken notice of the positive aspects of social networking such as creation of blogs and to support groups of system designers going through the same challenges and difficulties. This paper introduces a social networking framework for collaborative education, design and modeling of the next generation of smarter products and services. Human behaviour modeling in social networking application aims to ensure that human considerations for learners and designers have a prominent place in the integrated design and development of sustainable, smarter products throughout the total system lifecycle. Social networks blend self-directed learning and prescribed, existing information. The self-directed element creates interest within a learner and the ability to access existing information facilitates its transfer, and eventual retention of knowledge acquired.

Effect of resistant and digestible rice starches on human cytokine and lactate metabolic networks in serum.

PubMed

Wang, Huisong; Pang, Guangchang

2017-05-01

Resistant starch generated after treating ordinary starch is of great significance to human health in the countries with overnutrition. However, its functional evaluation in the human body has been rarely reported. By determining the lactate metabolic flux, 12 serum enzymes expression level and 38 serum cytokines in healthy volunteers, the variation in cytokine network and lactate metabolic network in serum were investigated to compare the mechanism of the physiological effects between the two starches. The results indicated that compared with digestible starch, resistant starch had anti-inflammatory effects, increased anabolism, and decreased catabolism. Further, the intercellular communication networks including cytokine and lactate metabolic networks were mapped out. The relationship suggested that resistant starch might affect and control the secretion of cytokines to regulate lactate metabolic network in the body, promoting the development of immunometabolism. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dynamic modeling and optimization for space logistics using time-expanded networks

NASA Astrophysics Data System (ADS)

Ho, Koki; de Weck, Olivier L.; Hoffman, Jeffrey A.; Shishko, Robert

2014-12-01

This research develops a dynamic logistics network formulation for lifecycle optimization of mission sequences as a system-level integrated method to find an optimal combination of technologies to be used at each stage of the campaign. This formulation can find the optimal transportation architecture considering its technology trades over time. The proposed methodologies are inspired by the ground logistics analysis techniques based on linear programming network optimization. Particularly, the time-expanded network and its extension are developed for dynamic space logistics network optimization trading the quality of the solution with the computational load. In this paper, the methodologies are applied to a human Mars exploration architecture design problem. The results reveal multiple dynamic system-level trades over time and give recommendation of the optimal strategy for the human Mars exploration architecture. The considered trades include those between In-Situ Resource Utilization (ISRU) and propulsion technologies as well as the orbit and depot location selections over time. This research serves as a precursor for eventual permanent settlement and colonization of other planets by humans and us becoming a multi-planet species.

Developmental Changes in Topological Asymmetry Between Hemispheric Brain White Matter Networks from Adolescence to Young Adulthood.

PubMed

Zhong, Suyu; He, Yong; Shu, Hua; Gong, Gaolang

2017-04-01

Human brain asymmetries have been well described. Intriguingly, a number of asymmetries in brain phenotypes have been shown to change throughout the lifespan. Recent studies have revealed topological asymmetries between hemispheric white matter networks in the human brain. However, it remains unknown whether and how these topological asymmetries evolve from adolescence to young adulthood, a critical period that constitutes the second peak of human brain and cognitive development. To address this question, the present study included a large cohort of healthy adolescents and young adults. Diffusion and structural magnetic resonance imaging were acquired to construct hemispheric white matter networks, and graph-theory was applied to quantify topological parameters of the hemispheric networks. In both adolescents and young adults, rightward asymmetry in both global and local network efficiencies was consistently observed between the 2 hemispheres, but the degree of the asymmetry was significantly decreased in young adults. At the nodal level, the young adults exhibited less rightward asymmetry of nodal efficiency mainly around the parasylvian area, posterior tempo-parietal cortex, and fusiform gyrus. These developmental patterns of network asymmetry provide novel insight into the human brain structural development from adolescence to young adulthood and also likely relate to the maturation of language and social cognition that takes place during this period. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Social networks and cooperation in hunter-gatherers.

PubMed

Apicella, Coren L; Marlowe, Frank W; Fowler, James H; Christakis, Nicholas A

2012-01-25

Social networks show striking structural regularities, and both theory and evidence suggest that networks may have facilitated the development of large-scale cooperation in humans. Here, we characterize the social networks of the Hadza, a population of hunter-gatherers in Tanzania. We show that Hadza networks have important properties also seen in modernized social networks, including a skewed degree distribution, degree assortativity, transitivity, reciprocity, geographic decay and homophily. We demonstrate that Hadza camps exhibit high between-group and low within-group variation in public goods game donations. Network ties are also more likely between people who give the same amount, and the similarity in cooperative behaviour extends up to two degrees of separation. Social distance appears to be as important as genetic relatedness and physical proximity in explaining assortativity in cooperation. Our results suggest that certain elements of social network structure may have been present at an early point in human history. Also, early humans may have formed ties with both kin and non-kin, based in part on their tendency to cooperate. Social networks may thus have contributed to the emergence of cooperation.

Genes uniquely expressed in human growth plate chondrocytes uncover a distinct regulatory network.

PubMed

Li, Bing; Balasubramanian, Karthika; Krakow, Deborah; Cohn, Daniel H

2017-12-20

Chondrogenesis is the earliest stage of skeletal development and is a highly dynamic process, integrating the activities and functions of transcription factors, cell signaling molecules and extracellular matrix proteins. The molecular mechanisms underlying chondrogenesis have been extensively studied and multiple key regulators of this process have been identified. However, a genome-wide overview of the gene regulatory network in chondrogenesis has not been achieved. In this study, employing RNA sequencing, we identified 332 protein coding genes and 34 long non-coding RNA (lncRNA) genes that are highly selectively expressed in human fetal growth plate chondrocytes. Among the protein coding genes, 32 genes were associated with 62 distinct human skeletal disorders and 153 genes were associated with skeletal defects in knockout mice, confirming their essential roles in skeletal formation. These gene products formed a comprehensive physical interaction network and participated in multiple cellular processes regulating skeletal development. The data also revealed 34 transcription factors and 11,334 distal enhancers that were uniquely active in chondrocytes, functioning as transcriptional regulators for the cartilage-selective genes. Our findings revealed a complex gene regulatory network controlling skeletal development whereby transcription factors, enhancers and lncRNAs participate in chondrogenesis by transcriptional regulation of key genes. Additionally, the cartilage-selective genes represent candidate genes for unsolved human skeletal disorders.

A human factors approach to range scheduling for satellite control

NASA Technical Reports Server (NTRS)

Wright, Cameron H. G.; Aitken, Donald J.

1991-01-01

Range scheduling for satellite control presents a classical problem: supervisory control of a large-scale dynamic system, with unwieldy amounts of interrelated data used as inputs to the decision process. Increased automation of the task, with the appropriate human-computer interface, is highly desirable. The development and user evaluation of a semi-automated network range scheduling system is described. The system incorporates a synergistic human-computer interface consisting of a large screen color display, voice input/output, a 'sonic pen' pointing device, a touchscreen color CRT, and a standard keyboard. From a human factors standpoint, this development represents the first major improvement in almost 30 years to the satellite control network scheduling task.

Revealing topological organization of human brain functional networks with resting-state functional near infrared spectroscopy.

PubMed

Niu, Haijing; Wang, Jinhui; Zhao, Tengda; Shu, Ni; He, Yong

2012-01-01

The human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics. We utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques. Our results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.

Resilience in social insect infrastructure systems

PubMed Central

2016-01-01

Both human and insect societies depend on complex and highly coordinated infrastructure systems, such as communication networks, supply chains and transportation networks. Like human-designed infrastructure systems, those of social insects are regularly subject to disruptions such as natural disasters, blockages or breaks in the transportation network, fluctuations in supply and/or demand, outbreaks of disease and loss of individuals. Unlike human-designed systems, there is no deliberate planning or centralized control system; rather, individual insects make simple decisions based on local information. How do these highly decentralized, leaderless systems deal with disruption? What factors make a social insect system resilient, and which factors lead to its collapse? In this review, we bring together literature on resilience in three key social insect infrastructure systems: transportation networks, supply chains and communication networks. We describe how systems differentially invest in three pathways to resilience: resistance, redirection or reconstruction. We suggest that investment in particular resistance pathways is related to the severity and frequency of disturbance. In the final section, we lay out a prospectus for future research. Human infrastructure networks are rapidly becoming decentralized and interconnected; indeed, more like social insect infrastructures. Human infrastructure management might therefore learn from social insect researchers, who can in turn make use of the mature analytical and simulation tools developed for the study of human infrastructure resilience. PMID:26962030

HExpoChem: a systems biology resource to explore human exposure to chemicals.

PubMed

Taboureau, Olivier; Jacobsen, Ulrik Plesner; Kalhauge, Christian; Edsgärd, Daniel; Rigina, Olga; Gupta, Ramneek; Audouze, Karine

2013-05-01

Humans are exposed to diverse hazardous chemicals daily. Although an exposure to these chemicals is suspected to have adverse effects on human health, mechanistic insights into how they interact with the human body are still limited. Therefore, acquisition of curated data and development of computational biology approaches are needed to assess the health risks of chemical exposure. Here we present HExpoChem, a tool based on environmental chemicals and their bioactivities on human proteins with the objective of aiding the qualitative exploration of human exposure to chemicals. The chemical-protein interactions have been enriched with a quality-scored human protein-protein interaction network, a protein-protein association network and a chemical-chemical interaction network, thus allowing the study of environmental chemicals through formation of protein complexes and phenotypic outcomes enrichment. HExpoChem is available at http://www.cbs.dtu.dk/services/HExpoChem-1.0/.

Overview of Aro Program on Network Science for Human Decision Making

NASA Astrophysics Data System (ADS)

West, Bruce J.

This program brings together researchers from disparate disciplines to work on a complex research problem that defies confinement within any single discipline. Consequently, not only are new and rewarding solutions sought and obtained for a problem of importance to society and the Army, that is, the human dimension of complex networks, but, in addition, collaborations are established that would not otherwise have formed given the traditional disciplinary compartmentalization of research. This program develops the basic research foundation of a science of networks supporting the linkage between the physical and human (cognitive and social) domains as they relate to human decision making. The strategy is to extend the recent methods of non-equilibrium statistical physics to non-stationary, renewal stochastic processes that appear to be characteristic of the interactions among nodes in complex networks. We also pursue understanding of the phenomenon of synchronization, whose mathematical formulation has recently provided insight into how complex networks reach accommodation and cooperation. The theoretical analyses of complex networks, although mathematically rigorous, often elude analytic solutions and require computer simulation and computation to analyze the underlying dynamic process.

Detecting phenotype-driven transitions in regulatory network structure.

PubMed

Padi, Megha; Quackenbush, John

2018-01-01

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular and contain dense "communities" of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks and analyzing their topologies separately, there is a lack of robust methods for quantifying differences in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. As an application, we use ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identifies modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors are enriched for genes associated with blood vessel development, and modules found in female breast tissue are enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules suggests that not only can ALPACA identify structural changes in complex networks, but also that these changes may be relevant for characterizing biological phenotypes.

Importance of Thickness in Human Cardiomyocyte Network for Effective Electrophysiological Stimulation Using On-Chip Extracellular Microelectrodes

NASA Astrophysics Data System (ADS)

Hamada, Tomoyo; Nomura, Fumimasa; Kaneko, Tomoyuki; Yasuda, Kenji

2012-06-01

We have developed a three-dimensionally controlled in vitro human cardiomyocyte network assay for the measurements of drug-induced conductivity changes and the appearance of fatal arrhythmia such as ventricular tachycardia/fibrillation for more precise in vitro predictive cardiotoxicity. To construct an artificial conductance propagation model of a human cardiomyocyte network, first, we examined the cell concentration dependence of the cell network heights and found the existence of a height limit of cell networks, which was double-layer height, whereas the cardiomyocytes were effectively and homogeneously cultivated within the microchamber maintaining their spatial distribution constant and their electrophysiological conductance and propagation were successfully recorded using a microelectrode array set on the bottom of the microchamber. The pacing ability of a cardiomyocyte's electrophysiological response has been evaluated using microelectrode extracellular stimulation, and the stimulation for pacing also successfully regulated the beating frequencies of two-layered cardiomyocyte networks, whereas monolayered cardiomyocyte networks were hardly stimulated by the external electrodes using the two-layered cardiomyocyte stimulation condition. The stability of the lined-up shape of human cardiomyocytes within the rectangularly arranged agarose microchambers was limited for a two-layered cardiomyocyte network because their stronger force generation shrunk those cells after peeling off the substrate. The results indicate the importance of fabrication technology of thickness control of cellular networks for effective extracellular stimulation and the potential concerning thick cardiomyocyte networks for long-term cultivation.

GABA and Gap Junctions in the Development of Synchronized Activity in Human Pluripotent Stem Cell-Derived Neural Networks

PubMed Central

Mäkinen, Meeri Eeva-Liisa; Ylä-Outinen, Laura; Narkilahti, Susanna

2018-01-01

The electrical activity of the brain arises from single neurons communicating with each other. However, how single neurons interact during early development to give rise to neural network activity remains poorly understood. We studied the emergence of synchronous neural activity in human pluripotent stem cell (hPSC)-derived neural networks simultaneously on a single-neuron level and network level. The contribution of gamma-aminobutyric acid (GABA) and gap junctions to the development of synchronous activity in hPSC-derived neural networks was studied with GABA agonist and antagonist and by blocking gap junctional communication, respectively. We characterized the dynamics of the network-wide synchrony in hPSC-derived neural networks with high spatial resolution (calcium imaging) and temporal resolution microelectrode array (MEA). We found that the emergence of synchrony correlates with a decrease in very strong GABA excitation. However, the synchronous network was found to consist of a heterogeneous mixture of synchronously active cells with variable responses to GABA, GABA agonists and gap junction blockers. Furthermore, we show how single-cell distributions give rise to the network effect of GABA, GABA agonists and gap junction blockers. Finally, based on our observations, we suggest that the earliest form of synchronous neuronal activity depends on gap junctions and a decrease in GABA induced depolarization but not on GABAA mediated signaling. PMID:29559893

Identification and functional analysis of long non-coding RNAs in human and mouse early embryos based on single-cell transcriptome data

PubMed Central

Qiu, Jia-jun; Ren, Zhao-rui; Yan, Jing-bin

2016-01-01

Epigenetics regulations have an important role in fertilization and proper embryonic development, and several human diseases are associated with epigenetic modification disorders, such as Rett syndrome, Beckwith-Wiedemann syndrome and Angelman syndrome. However, the dynamics and functions of long non-coding RNAs (lncRNAs), one type of epigenetic regulators, in human pre-implantation development have not yet been demonstrated. In this study, a comprehensive analysis of human and mouse early-stage embryonic lncRNAs was performed based on public single-cell RNA sequencing data. Expression profile analysis revealed that lncRNAs are expressed in a developmental stage–specific manner during human early-stage embryonic development, whereas a more temporal-specific expression pattern was identified in mouse embryos. Weighted gene co-expression network analysis suggested that lncRNAs involved in human early-stage embryonic development are associated with several important functions and processes, such as oocyte maturation, zygotic genome activation and mitochondrial functions. We also found that the network of lncRNAs involved in zygotic genome activation was highly preservative between human and mouse embryos, whereas in other stages no strong correlation between human and mouse embryo was observed. This study provides insight into the molecular mechanism underlying lncRNA involvement in human pre-implantation embryonic development. PMID:27542205

Building a Smartphone Seismic Network

NASA Astrophysics Data System (ADS)

Kong, Q.; Allen, R. M.

2013-12-01

We are exploring to build a new type of seismic network by using the smartphones. The accelerometers in smartphones can be used to record earthquakes, the GPS unit can give an accurate location, and the built-in communication unit makes the communication easier for this network. In the future, these smartphones may work as a supplement network to the current traditional network for scientific research and real-time applications. In order to build this network, we developed an application for android phones and server to record the acceleration in real time. These records can be sent back to a server in real time, and analyzed at the server. We evaluated the performance of the smartphone as a seismic recording instrument by comparing them with high quality accelerometer while located on controlled shake tables for a variety of tests, and also the noise floor test. Based on the daily human activity data recorded by the volunteers and the shake table tests data, we also developed algorithm for the smartphones to detect earthquakes from daily human activities. These all form the basis of setting up a new prototype smartphone seismic network in the near future.

Network Science Based Quantification of Resilience Demonstrated on the Indian Railways Network.

PubMed

Bhatia, Udit; Kumar, Devashish; Kodra, Evan; Ganguly, Auroop R

2015-01-01

The structure, interdependence, and fragility of systems ranging from power-grids and transportation to ecology, climate, biology and even human communities and the Internet have been examined through network science. While response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science based quantitative framework for measuring, comparing and interpreting hazard responses as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario illustrate hazard responses and effectiveness of proposed recovery strategies. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of these strategies suggests that faster and more efficient recovery is possible through network centrality measures. Optimal recovery strategies may be different per hazard, per community within a network, and for different measures of partial recovery. In addition, topological characterization provides a means for interpreting the comparative performance of proposed recovery strategies. The methods can be directly extended to other Large-Scale Critical Lifeline Infrastructure Networks including transportation, water, energy and communications systems that are threatened by natural or human-induced hazards, including cascading failures. Furthermore, the quantitative framework developed here can generalize across natural, engineered and human systems, offering an actionable and generalizable approach for emergency management in particular as well as for network resilience in general.

LENS: web-based lens for enrichment and network studies of human proteins

PubMed Central

2015-01-01

Background Network analysis is a common approach for the study of genetic view of diseases and biological pathways. Typically, when a set of genes are identified to be of interest in relation to a disease, say through a genome wide association study (GWAS) or a different gene expression study, these genes are typically analyzed in the context of their protein-protein interaction (PPI) networks. Further analysis is carried out to compute the enrichment of known pathways and disease-associations in the network. Having tools for such analysis at the fingertips of biologists without the requirement for computer programming or curation of data would accelerate the characterization of genes of interest. Currently available tools do not integrate network and enrichment analysis and their visualizations, and most of them present results in formats not most conducive to human cognition. Results We developed the tool Lens for Enrichment and Network Studies of human proteins (LENS) that performs network and pathway and diseases enrichment analyses on genes of interest to users. The tool creates a visualization of the network, provides easy to read statistics on network connectivity, and displays Venn diagrams with statistical significance values of the network's association with drugs, diseases, pathways, and GWASs. We used the tool to analyze gene sets related to craniofacial development, autism, and schizophrenia. Conclusion LENS is a web-based tool that does not require and download or plugins to use. The tool is free and does not require login for use, and is available at http://severus.dbmi.pitt.edu/LENS. PMID:26680011

Network Science Based Quantification of Resilience Demonstrated on the Indian Railways Network

PubMed Central

Bhatia, Udit; Kumar, Devashish; Kodra, Evan; Ganguly, Auroop R.

2015-01-01

The structure, interdependence, and fragility of systems ranging from power-grids and transportation to ecology, climate, biology and even human communities and the Internet have been examined through network science. While response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science based quantitative framework for measuring, comparing and interpreting hazard responses as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario illustrate hazard responses and effectiveness of proposed recovery strategies. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of these strategies suggests that faster and more efficient recovery is possible through network centrality measures. Optimal recovery strategies may be different per hazard, per community within a network, and for different measures of partial recovery. In addition, topological characterization provides a means for interpreting the comparative performance of proposed recovery strategies. The methods can be directly extended to other Large-Scale Critical Lifeline Infrastructure Networks including transportation, water, energy and communications systems that are threatened by natural or human-induced hazards, including cascading failures. Furthermore, the quantitative framework developed here can generalize across natural, engineered and human systems, offering an actionable and generalizable approach for emergency management in particular as well as for network resilience in general. PMID:26536227

Fractal dimension of the middle meningeal vessels: variation and evolution in Homo erectus, Neanderthals, and modern humans.

PubMed

Bruner, Emiliano; Mantini, Simone; Perna, Agostino; Maffei, Carlotta; Manzi, Giorgio

2005-01-01

The middle meningeal vascular network leaves its traces on the endocranial surface because of the tight relationship between neurocranial development and brain growth. Analysing the endocast of fossil specimens, it is therefore possible to describe the morphology of these structures, leading inferences on the cerebral physiology and metabolism in extinct human groups. In this paper, general features of the meningeal vascular traces are described for specimens included in the Homo erectus, Homo neanderthalensis, and Homo sapiens hypodigms. The complexity of the arterial network is quantified by its fractal dimension, calculated through the box-counting method. Modern humans show significant differences from the other two taxa because of the anterior vascular dominance and the larger fractal dimension. Neither the fractal dimension nor the anterior development are merely associated with cranial size increase. Considering the differences between Neanderthals and modern humans, these results may be interpreted in terms of phylogeny, cerebral functions, or cranial structural network.

Creating Communications, Computing, and Networking Technology Development Road Maps for Future NASA Human and Robotic Missions

NASA Technical Reports Server (NTRS)

Bhasin, Kul; Hayden, Jeffrey L.

2005-01-01

For human and robotic exploration missions in the Vision for Exploration, roadmaps are needed for capability development and investments based on advanced technology developments. A roadmap development process was undertaken for the needed communications, and networking capabilities and technologies for the future human and robotics missions. The underlying processes are derived from work carried out during development of the future space communications architecture, an d NASA's Space Architect Office (SAO) defined formats and structures for accumulating data. Interrelationships were established among emerging requirements, the capability analysis and technology status, and performance data. After developing an architectural communications and networking framework structured around the assumed needs for human and robotic exploration, in the vicinity of Earth, Moon, along the path to Mars, and in the vicinity of Mars, information was gathered from expert participants. This information was used to identify the capabilities expected from the new infrastructure and the technological gaps in the way of obtaining them. We define realistic, long-term space communication architectures based on emerging needs and translate the needs into interfaces, functions, and computer processing that will be required. In developing our roadmapping process, we defined requirements for achieving end-to-end activities that will be carried out by future NASA human and robotic missions. This paper describes: 10 the architectural framework developed for analysis; 2) our approach to gathering and analyzing data from NASA, industry, and academia; 3) an outline of the technology research to be done, including milestones for technology research and demonstrations with timelines; and 4) the technology roadmaps themselves.

Simple deterministic models and applications. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Yang, Hyun Mo

2015-12-01

Currently, discrete modellings are largely accepted due to the access to computers with huge storage capacity and high performance processors and easy implementation of algorithms, allowing to develop and simulate increasingly sophisticated models. Wang et al. [7] present a review of dynamics in complex networks, focusing on the interaction between disease dynamics and human behavioral and social dynamics. By doing an extensive review regarding to the human behavior responding to disease dynamics, the authors briefly describe the complex dynamics found in the literature: well-mixed populations networks, where spatial structure can be neglected, and other networks considering heterogeneity on spatially distributed populations. As controlling mechanisms are implemented, such as social distancing due 'social contagion', quarantine, non-pharmaceutical interventions and vaccination, adaptive behavior can occur in human population, which can be easily taken into account in the dynamics formulated by networked populations.

Predicting Networked Strategic Behavior via Machine Learning and Game Theory

DTIC Science & Technology

2015-01-13

The funding for this project was used to develop basic models, methodology and algorithms for the application of machine learning and related tools to settings in which strategic behavior is central. Among the topics studied was the development of simple behavioral models explaining and predicting human subject behavior in networked strategic experiments from prior work. These included experiments in biased voting and networked trading, among others.

77 FR 52337 - Eunice Kennedy Shriver National Institute of Child Health & Human Development Notice of Closed...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-29

... Institute of Child Health and Human Development Special Emphasis Panel; Contraceptive Clinical Trials Network (Male Studies) September. Date: September 27, 2012. Time: 2 p.m. to 4 p.m. Agenda: To review and...

Resting state brain networks in the prairie vole.

PubMed

Ortiz, Juan J; Portillo, Wendy; Paredes, Raul G; Young, Larry J; Alcauter, Sarael

2018-01-19

Resting state functional magnetic resonance imaging (rsfMRI) has shown the hierarchical organization of the human brain into large-scale complex networks, referred as resting state networks. This technique has turned into a promising translational research tool after the finding of similar resting state networks in non-human primates, rodents and other animal models of great value for neuroscience. Here, we demonstrate and characterize the presence of resting states networks in Microtus ochrogaster, the prairie vole, an extraordinary animal model to study complex human-like social behavior, with potential implications for the research of normal social development, addiction and neuropsychiatric disorders. Independent component analysis of rsfMRI data from isoflurane-anestethized prairie voles resulted in cortical and subcortical networks, including primary motor and sensory networks, but also included putative salience and default mode networks. We further discuss how future research could help to close the gap between the properties of the large scale functional organization and the underlying neurobiology of several aspects of social cognition. These results contribute to the evidence of preserved resting state brain networks across species and provide the foundations to explore the use of rsfMRI in the prairie vole for basic and translational research.

Decoding the non-coding RNAs in Alzheimer's disease.

PubMed

Schonrock, Nicole; Götz, Jürgen

2012-11-01

Non-coding RNAs (ncRNAs) are integral components of biological networks with fundamental roles in regulating gene expression. They can integrate sequence information from the DNA code, epigenetic regulation and functions of multimeric protein complexes to potentially determine the epigenetic status and transcriptional network in any given cell. Humans potentially contain more ncRNAs than any other species, especially in the brain, where they may well play a significant role in human development and cognitive ability. This review discusses their emerging role in Alzheimer's disease (AD), a human pathological condition characterized by the progressive impairment of cognitive functions. We discuss the complexity of the ncRNA world and how this is reflected in the regulation of the amyloid precursor protein and Tau, two proteins with central functions in AD. By understanding this intricate regulatory network, there is hope for a better understanding of disease mechanisms and ultimately developing diagnostic and therapeutic tools.

Computational Framework for Analysis of Prey–Prey Associations in Interaction Proteomics Identifies Novel Human Protein–Protein Interactions and Networks

PubMed Central

Saha, Sudipto; Dazard, Jean-Eudes; Xu, Hua; Ewing, Rob M.

2013-01-01

Large-scale protein–protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific “bait” protein and its associated “prey” proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait–prey and cocomplexed preys (prey–prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein–protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait–prey and prey–prey interactions can be used to refine network topology and extend known protein networks. PMID:22845868

Resilience in social insect infrastructure systems.

PubMed

Middleton, Eliza J T; Latty, Tanya

2016-03-01

Both human and insect societies depend on complex and highly coordinated infrastructure systems, such as communication networks, supply chains and transportation networks. Like human-designed infrastructure systems, those of social insects are regularly subject to disruptions such as natural disasters, blockages or breaks in the transportation network, fluctuations in supply and/or demand, outbreaks of disease and loss of individuals. Unlike human-designed systems, there is no deliberate planning or centralized control system; rather, individual insects make simple decisions based on local information. How do these highly decentralized, leaderless systems deal with disruption? What factors make a social insect system resilient, and which factors lead to its collapse? In this review, we bring together literature on resilience in three key social insect infrastructure systems: transportation networks, supply chains and communication networks. We describe how systems differentially invest in three pathways to resilience: resistance, redirection or reconstruction. We suggest that investment in particular resistance pathways is related to the severity and frequency of disturbance. In the final section, we lay out a prospectus for future research. Human infrastructure networks are rapidly becoming decentralized and interconnected; indeed, more like social insect infrastructures. Human infrastructure management might therefore learn from social insect researchers, who can in turn make use of the mature analytical and simulation tools developed for the study of human infrastructure resilience. © 2016 The Author(s).

An Implementation of Wireless Body Area Networks for Improving Priority Data Transmission Delay.

PubMed

Gündoğdu, Köksal; Çalhan, Ali

2016-03-01

The rapid growth of wireless sensor networks has enabled the human health monitoring of patients using body sensor nodes that gather and evaluate human body parameters and movements. This study describes both simulation model and implementation of a new traffic sensitive wireless body area network by using non-preemptive priority queue discipline. A wireless body area network implementation employing TDMA is designed with three different priorities of data traffics. Besides, a coordinator node having the non-preemptive priority queue is performed in this study. We have also developed, modeled and simulated example network scenarios by using the Riverbed Modeler simulation software with the purpose of verifying the implementation results. The simulation results obtained under various network load conditions are consistent with the implementation results.

A social network model for the development of a 'Theory of Mind'

NASA Astrophysics Data System (ADS)

Harré, Michael S.

2013-02-01

A "Theory of Mind" is one of the most important skills we as humans have developed; It enables us to infer the mental states and intentions of others, build stable networks of relationships and it plays a central role in our psychological make-up and development. Findings published earlier this year have also shown that we as a species as well as each of us individually benefit from the enlargement of the underlying neuro-anatomical regions that support our social networks, mediated by our Theory of Mind that stabilises these networks. On the basis of such progress and that of earlier work, this paper draws together several different strands from psychology, behavioural economics and network theory in order to generate a novel theoretical representation of the development of our social-cognition and how subsequent larger social networks enables much of our cultural development but at the increased risk of mental disorders.

Rich-club organization of the newborn human brain

PubMed Central

Ball, Gareth; Aljabar, Paul; Zebari, Sally; Tusor, Nora; Arichi, Tomoki; Merchant, Nazakat; Robinson, Emma C.; Ogundipe, Enitan; Rueckert, Daniel; Edwards, A. David; Counsell, Serena J.

2014-01-01

Combining diffusion magnetic resonance imaging and network analysis in the adult human brain has identified a set of highly connected cortical hubs that form a “rich club”—a high-cost, high-capacity backbone thought to enable efficient network communication. Rich-club architecture appears to be a persistent feature of the mature mammalian brain, but it is not known when this structure emerges during human development. In this longitudinal study we chart the emergence of structural organization in mid to late gestation. We demonstrate that a rich club of interconnected cortical hubs is already present by 30 wk gestation. Subsequently, until the time of normal birth, the principal development is a proliferation of connections between core hubs and the rest of the brain. We also consider the impact of environmental factors on early network development, and compare term-born neonates to preterm infants at term-equivalent age. Though rich-club organization remains intact following premature birth, we reveal significant disruptions in both in cortical–subcortical connectivity and short-distance corticocortical connections. Rich club organization is present well before the normal time of birth and may provide the fundamental structural architecture for the subsequent emergence of complex neurological functions. Premature exposure to the extrauterine environment is associated with altered network architecture and reduced network capacity, which may in part account for the high prevalence of cognitive problems in preterm infants. PMID:24799693

Learning for Work and Professional Development: The Significance of Informal Learning Networks of Digital Media Industry Professionals

ERIC Educational Resources Information Center

Campana, Joe

2014-01-01

Informal learning networks play a key role in the skill and professional development of professionals, working in micro-businesses within Australia's digital media industry, as they do not have access to learning and development or human resources sections that can assist in mapping their learning pathway. Professionals working in this environment…

Do network relationships matter? Comparing network and instream habitat variables to explain densities of juvenile coho salmon (Oncorhynchus kisutch) in mid-coastal Oregon, USA

Treesearch

Rebecca L. Flitcroft; Kelly M. Burnett; Gordon H. Reeves; Lisa M. Ganio

2012-01-01

Aquatic ecologists are working to develop theory and techniques for analysis of dynamic stream processes and communities of organisms. Such work is critical for the development of conservation plans that are relevant at the scale of entire ecosystems. The stream network is the foundation upon which stream systems are organized. Natural and human disturbances in streams...

Agricultural trade networks and patterns of economic development.

PubMed

Shutters, Shade T; Muneepeerakul, Rachata

2012-01-01

International trade networks are manifestations of a complex combination of diverse underlying factors, both natural and social. Here we apply social network analytics to the international trade network of agricultural products to better understand the nature of this network and its relation to patterns of international development. Using a network tool known as triadic analysis we develop triad significance profiles for a series of agricultural commodities traded among countries. Results reveal a novel network "superfamily" combining properties of biological information processing networks and human social networks. To better understand this unique network signature, we examine in more detail the degree and triadic distributions within the trade network by country and commodity. Our results show that countries fall into two very distinct classes based on their triadic frequencies. Roughly 165 countries fall into one class while 18, all highly isolated with respect to international agricultural trade, fall into the other. Only Vietnam stands out as a unique case. Finally, we show that as a country becomes less isolated with respect to number of trading partners, the country's triadic signature follows a predictable trajectory that may correspond to a trajectory of development.

Cell diversity and network dynamics in photosensitive human brain organoids

PubMed Central

Quadrato, Giorgia; Nguyen, Tuan; Macosko, Evan Z.; Sherwood, John L.; Yang, Sung Min; Berger, Daniel; Maria, Natalie; Scholvin, Jorg; Goldman, Melissa; Kinney, Justin; Boyden, Edward S.; Lichtman, Jeff; Williams, Ziv M.; McCarroll, Steven A.; Arlotta, Paola

2017-01-01

In vitro models of the developing brain such as 3D brain organoids offer an unprecedented opportunity to study aspects of human brain development and disease. However, it remains undefined what cells are generated within organoids and to what extent they recapitulate the regional complexity, cellular diversity, and circuit functionality of the brain. Here, we analyzed gene expression in over 80,000 individual cells isolated from 31 human brain organoids. We find that organoids can generate a broad diversity of cells, which are related to endogenous classes, including cells from the cerebral cortex and the retina. Organoids could be developed over extended periods (over 9 months) enabling unprecedented levels of maturity including the formation of dendritic spines and of spontaneously-active neuronal networks. Finally, neuronal activity within organoids could be controlled using light stimulation of photoreceptor-like cells, which may offer ways to probe the functionality of human neuronal circuits using physiological sensory stimuli. PMID:28445462

Cell diversity and network dynamics in photosensitive human brain organoids.

PubMed

Quadrato, Giorgia; Nguyen, Tuan; Macosko, Evan Z; Sherwood, John L; Min Yang, Sung; Berger, Daniel R; Maria, Natalie; Scholvin, Jorg; Goldman, Melissa; Kinney, Justin P; Boyden, Edward S; Lichtman, Jeff W; Williams, Ziv M; McCarroll, Steven A; Arlotta, Paola

2017-05-04

In vitro models of the developing brain such as three-dimensional brain organoids offer an unprecedented opportunity to study aspects of human brain development and disease. However, the cells generated within organoids and the extent to which they recapitulate the regional complexity, cellular diversity and circuit functionality of the brain remain undefined. Here we analyse gene expression in over 80,000 individual cells isolated from 31 human brain organoids. We find that organoids can generate a broad diversity of cells, which are related to endogenous classes, including cells from the cerebral cortex and the retina. Organoids could be developed over extended periods (more than 9 months), allowing for the establishment of relatively mature features, including the formation of dendritic spines and spontaneously active neuronal networks. Finally, neuronal activity within organoids could be controlled using light stimulation of photosensitive cells, which may offer a way to probe the functionality of human neuronal circuits using physiological sensory stimuli.

Development of a computational model on the neural activity patterns of a visual working memory in a hierarchical feedforward Network

NASA Astrophysics Data System (ADS)

An, Soyoung; Choi, Woochul; Paik, Se-Bum

2015-11-01

Understanding the mechanism of information processing in the human brain remains a unique challenge because the nonlinear interactions between the neurons in the network are extremely complex and because controlling every relevant parameter during an experiment is difficult. Therefore, a simulation using simplified computational models may be an effective approach. In the present study, we developed a general model of neural networks that can simulate nonlinear activity patterns in the hierarchical structure of a neural network system. To test our model, we first examined whether our simulation could match the previously-observed nonlinear features of neural activity patterns. Next, we performed a psychophysics experiment for a simple visual working memory task to evaluate whether the model could predict the performance of human subjects. Our studies show that the model is capable of reproducing the relationship between memory load and performance and may contribute, in part, to our understanding of how the structure of neural circuits can determine the nonlinear neural activity patterns in the human brain.

Deep Neural Networks as a Computational Model for Human Shape Sensitivity

PubMed Central

Op de Beeck, Hans P.

2016-01-01

Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional ‘deep’ neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development. PMID:27124699

Science and Environment Education Views from Developing Countries. Secondary Education Series.

ERIC Educational Resources Information Center

Ware, Sylvia A., Ed.

This document is the first in the Secondary Education Series and is a product of the cooperation between the Human Development Network Education Team and the Human Development Sector Unit of the Latin American and Caribbean Region. The purpose of this book is to demonstrate the present valuable information found in developing nations and the…

Dynamic reconfiguration of frontal brain networks during executive cognition in humans

PubMed Central

Braun, Urs; Schäfer, Axel; Walter, Henrik; Erk, Susanne; Romanczuk-Seiferth, Nina; Haddad, Leila; Schweiger, Janina I.; Grimm, Oliver; Heinz, Andreas; Tost, Heike; Meyer-Lindenberg, Andreas; Bassett, Danielle S.

2015-01-01

The brain is an inherently dynamic system, and executive cognition requires dynamically reconfiguring, highly evolving networks of brain regions that interact in complex and transient communication patterns. However, a precise characterization of these reconfiguration processes during cognitive function in humans remains elusive. Here, we use a series of techniques developed in the field of “dynamic network neuroscience” to investigate the dynamics of functional brain networks in 344 healthy subjects during a working-memory challenge (the “n-back” task). In contrast to a control condition, in which dynamic changes in cortical networks were spread evenly across systems, the effortful working-memory condition was characterized by a reconfiguration of frontoparietal and frontotemporal networks. This reconfiguration, which characterizes “network flexibility,” employs transient and heterogeneous connectivity between frontal systems, which we refer to as “integration.” Frontal integration predicted neuropsychological measures requiring working memory and executive cognition, suggesting that dynamic network reconfiguration between frontal systems supports those functions. Our results characterize dynamic reconfiguration of large-scale distributed neural circuits during executive cognition in humans and have implications for understanding impaired cognitive function in disorders affecting connectivity, such as schizophrenia or dementia. PMID:26324898

Social Networks and Cooperation in Hunter-Gatherers

PubMed Central

Apicella, Coren L.; Marlowe, Frank W.; Fowler, James H.; Christakis, Nicholas A.

2011-01-01

Social networks exhibit striking structural regularities1,2, and theory and evidence suggest that they may have played a role in the development of large-scale cooperation in humans3–7. Here, we characterize the social networks of the Hadza, an evolutionarily relevant population of hunter-gatherers8. We show that Hadza networks exhibit important properties also seen in modernized networks, including a skewed degree distribution, degree assortativity, transitivity, reciprocity, geographic decay, and homophily. Moreover, we demonstrate that Hadza camps exhibit high between-group and low within-group variation in public goods game donations. Network ties are also more likely between people who give the same amount, and the similarity in cooperative behaviour extends up to two degrees of separation. Finally, social distance appears to be as important as genetic relatedness and physical proximity in explaining assortativity in cooperation. Our results suggest that certain elements of social network structure may have been present at an early point in human history; that early humans may have formed ties with both kin and non-kin based, in part, on their tendency to cooperate; and that social networks may have contributed to the emergence of cooperation. PMID:22281599

Endothelial network formed with human dermal microvascular endothelial cells in autologous multicellular skin substitutes.

PubMed

Ponec, Maria; El Ghalbzouri, Abdoelwaheb; Dijkman, Remco; Kempenaar, Johanna; van der Pluijm, Gabri; Koolwijk, Pieter

2004-01-01

A human skin equivalent from a single skin biopsy harboring keratinocytes and melanocytes in the epidermal compartment, and fibroblasts and microvascular dermal endothelial cells in the dermal compartment was developed. The results of the study revealed that the nature of the extracellular matrix of the dermal compartments plays an important role in establishment of endothelial network in vitro. With rat-tail type I collagen matrices only lateral but not vertical expansion of endothelial networks was observed. In contrast, the presence of extracellular matrix of entirely human origin facilitated proper spatial organization of the endothelial network. Namely, when human dermal fibroblasts and microvascular endothelial cells were seeded on the bottom of an inert filter and subsequently epidermal cells were seeded on top of it, fibroblasts produced extracellular matrix throughout which numerous branched tubes were spreading three-dimensionally. Fibroblasts also facilitated the formation of basement membrane at the epidermal/matrix interface. Under all culture conditions, fully differentiated epidermis was formed with numerous melanocytes present in the basal epidermal cell layer. The results of the competitive RT-PCR revealed that both keratinocytes and fibroblasts expressed VEGF-A, -B, -C, aFGF and bFGF mRNA, whereas fibroblasts also expressed VEGF-D mRNA. At protein level, keratinocytes produced 10 times higher amounts of VEGF-A than fibroblasts did. The generation of multicellular skin equivalent from a single human skin biopsy will stimulate further developments for its application in the treatment of full-thickness skin defects. The potential development of biodegradable, biocompatible material suitable for these purposes is a great challenge for future research.

NKX2-5 regulates human cardiomyogenesis via a HEY2 dependent transcriptional network.

PubMed

Anderson, David J; Kaplan, David I; Bell, Katrina M; Koutsis, Katerina; Haynes, John M; Mills, Richard J; Phelan, Dean G; Qian, Elizabeth L; Leitoguinho, Ana Rita; Arasaratnam, Deevina; Labonne, Tanya; Ng, Elizabeth S; Davis, Richard P; Casini, Simona; Passier, Robert; Hudson, James E; Porrello, Enzo R; Costa, Mauro W; Rafii, Arash; Curl, Clare L; Delbridge, Lea M; Harvey, Richard P; Oshlack, Alicia; Cheung, Michael M; Mummery, Christine L; Petrou, Stephen; Elefanty, Andrew G; Stanley, Edouard G; Elliott, David A

2018-04-10

Congenital heart defects can be caused by mutations in genes that guide cardiac lineage formation. Here, we show deletion of NKX2-5, a critical component of the cardiac gene regulatory network, in human embryonic stem cells (hESCs), results in impaired cardiomyogenesis, failure to activate VCAM1 and to downregulate the progenitor marker PDGFRα. Furthermore, NKX2-5 null cardiomyocytes have abnormal physiology, with asynchronous contractions and altered action potentials. Molecular profiling and genetic rescue experiments demonstrate that the bHLH protein HEY2 is a key mediator of NKX2-5 function during human cardiomyogenesis. These findings identify HEY2 as a novel component of the NKX2-5 cardiac transcriptional network, providing tangible evidence that hESC models can decipher the complex pathways that regulate early stage human heart development. These data provide a human context for the evaluation of pathogenic mutations in congenital heart disease.

An ANOVA approach for statistical comparisons of brain networks.

PubMed

Fraiman, Daniel; Fraiman, Ricardo

2018-03-16

The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. In this paper, we focus on the statistical comparison of brain networks in a nonparametric framework and discuss the associated detection and identification problems. We tested network differences between groups with an analysis of variance (ANOVA) test we developed specifically for networks. We also propose and analyse the behaviour of a new statistical procedure designed to identify different subnetworks. As an example, we show the application of this tool in resting-state fMRI data obtained from the Human Connectome Project. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables. Our method can also be applied to other kind of networks such as protein interaction networks, gene networks or social networks.

Stimulus-Elicited Connectivity Influences Resting-State Connectivity Years Later in Human Development: A Prospective Study.

PubMed

Gabard-Durnam, Laurel Joy; Gee, Dylan Grace; Goff, Bonnie; Flannery, Jessica; Telzer, Eva; Humphreys, Kathryn Leigh; Lumian, Daniel Stephen; Fareri, Dominic Stephen; Caldera, Christina; Tottenham, Nim

2016-04-27

Although the functional architecture of the brain is indexed by resting-state connectivity networks, little is currently known about the mechanisms through which these networks assemble into stable mature patterns. The current study posits and tests the long-term phasic molding hypothesis that resting-state networks are gradually shaped by recurring stimulus-elicited connectivity across development by examining how both stimulus-elicited and resting-state functional connections of the human brain emerge over development at the systems level. Using a sequential design following 4- to 18-year-olds over a 2 year period, we examined the predictive associations between stimulus-elicited and resting-state connectivity in amygdala-cortical circuitry as an exemplar case (given this network's protracted development across these ages). Age-related changes in amygdala functional connectivity converged on the same regions of medial prefrontal cortex (mPFC) and inferior frontal gyrus when elicited by emotional stimuli and when measured at rest. Consistent with the long-term phasic molding hypothesis, prospective analyses for both connections showed that the magnitude of an individual's stimulus-elicited connectivity unidirectionally predicted resting-state functional connectivity 2 years later. For the amygdala-mPFC connection, only stimulus-elicited connectivity during childhood and the transition to adolescence shaped future resting-state connectivity, consistent with a sensitive period ending with adolescence for the amygdala-mPFC circuit. Together, these findings suggest that resting-state functional architecture may arise from phasic patterns of functional connectivity elicited by environmental stimuli over the course of development on the order of years. A fundamental issue in understanding the ontogeny of brain function is how resting-state (intrinsic) functional networks emerge and relate to stimulus-elicited functional connectivity. Here, we posit and test the long-term phasic molding hypothesis that resting-state network development is influenced by recurring stimulus-elicited connectivity through prospective examination of the developing human amygdala-cortical functional connections. Our results provide critical insight into how early environmental events sculpt functional network architecture across development and highlight childhood as a potential developmental period of heightened malleability for the amygdala-medial prefrontal cortex circuit. These findings have implications for how both positive and adverse experiences influence the developing brain and motivate future investigations of whether this molding mechanism reflects a general phenomenon of brain development. Copyright © 2016 the authors 0270-6474/16/364772-14$15.00/0.

Stimulus-Elicited Connectivity Influences Resting-State Connectivity Years Later in Human Development: A Prospective Study

PubMed Central

Gee, Dylan Grace; Goff, Bonnie; Flannery, Jessica; Telzer, Eva; Humphreys, Kathryn Leigh; Lumian, Daniel Stephen; Fareri, Dominic Stephen; Caldera, Christina; Tottenham, Nim

2016-01-01

Although the functional architecture of the brain is indexed by resting-state connectivity networks, little is currently known about the mechanisms through which these networks assemble into stable mature patterns. The current study posits and tests the long-term phasic molding hypothesis that resting-state networks are gradually shaped by recurring stimulus-elicited connectivity across development by examining how both stimulus-elicited and resting-state functional connections of the human brain emerge over development at the systems level. Using a sequential design following 4- to 18-year-olds over a 2 year period, we examined the predictive associations between stimulus-elicited and resting-state connectivity in amygdala-cortical circuitry as an exemplar case (given this network's protracted development across these ages). Age-related changes in amygdala functional connectivity converged on the same regions of medial prefrontal cortex (mPFC) and inferior frontal gyrus when elicited by emotional stimuli and when measured at rest. Consistent with the long-term phasic molding hypothesis, prospective analyses for both connections showed that the magnitude of an individual's stimulus-elicited connectivity unidirectionally predicted resting-state functional connectivity 2 years later. For the amygdala-mPFC connection, only stimulus-elicited connectivity during childhood and the transition to adolescence shaped future resting-state connectivity, consistent with a sensitive period ending with adolescence for the amygdala-mPFC circuit. Together, these findings suggest that resting-state functional architecture may arise from phasic patterns of functional connectivity elicited by environmental stimuli over the course of development on the order of years. SIGNIFICANCE STATEMENT A fundamental issue in understanding the ontogeny of brain function is how resting-state (intrinsic) functional networks emerge and relate to stimulus-elicited functional connectivity. Here, we posit and test the long-term phasic molding hypothesis that resting-state network development is influenced by recurring stimulus-elicited connectivity through prospective examination of the developing human amygdala-cortical functional connections. Our results provide critical insight into how early environmental events sculpt functional network architecture across development and highlight childhood as a potential developmental period of heightened malleability for the amygdala-medial prefrontal cortex circuit. These findings have implications for how both positive and adverse experiences influence the developing brain and motivate future investigations of whether this molding mechanism reflects a general phenomenon of brain development. PMID:27122035

Extending unified-theory-of-reinforcement neural networks to steady-state operant behavior.

PubMed

Calvin, Olivia L; McDowell, J J

2016-06-01

The unified theory of reinforcement has been used to develop models of behavior over the last 20 years (Donahoe et al., 1993). Previous research has focused on the theory's concordance with the respondent behavior of humans and animals. In this experiment, neural networks were developed from the theory to extend the unified theory of reinforcement to operant behavior on single-alternative variable-interval schedules. This area of operant research was selected because previously developed neural networks could be applied to it without significant alteration. Previous research with humans and animals indicates that the pattern of their steady-state behavior is hyperbolic when plotted against the obtained rate of reinforcement (Herrnstein, 1970). A genetic algorithm was used in the first part of the experiment to determine parameter values for the neural networks, because values that were used in previous research did not result in a hyperbolic pattern of behavior. After finding these parameters, hyperbolic and other similar functions were fitted to the behavior produced by the neural networks. The form of the neural network's behavior was best described by an exponentiated hyperbola (McDowell, 1986; McLean and White, 1983; Wearden, 1981), which was derived from the generalized matching law (Baum, 1974). In post-hoc analyses the addition of a baseline rate of behavior significantly improved the fit of the exponentiated hyperbola and removed systematic residuals. The form of this function was consistent with human and animal behavior, but the estimated parameter values were not. Copyright © 2016 Elsevier B.V. All rights reserved.

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

PubMed Central

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-01-01

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches. PMID:27792136

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-10-25

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis

PubMed Central

Sung, Jaeyun; Kim, Seunghyeon; Cabatbat, Josephine Jill T.; Jang, Sungho; Jin, Yong-Su; Jung, Gyoo Yeol; Chia, Nicholas; Kim, Pan-Jun

2017-01-01

A system-level framework of complex microbe–microbe and host–microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut. PMID:28585563

Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis.

PubMed

Sung, Jaeyun; Kim, Seunghyeon; Cabatbat, Josephine Jill T; Jang, Sungho; Jin, Yong-Su; Jung, Gyoo Yeol; Chia, Nicholas; Kim, Pan-Jun

2017-06-06

A system-level framework of complex microbe-microbe and host-microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.

Human Dignity and Humiliation Studies: A Global Network Advancing Dignity through Dialogue

ERIC Educational Resources Information Center

Lindner, Evelin G.; Hartling, Linda M.; Spalthoff, Ulrich

2011-01-01

Human rights are universally based on the concept of human dignity. Various international organizations are developing the theoretical, legal, and political framework for human rights. The underlying concept of human dignity is less disputed, but also receives less attention. This shortcoming is addressed by a worldwide group of scholars and…

Wireless Network for Measurement of Whole-Body Vibration

PubMed Central

Koenig, Diogo; Chiaramonte, Marilda S.; Balbinot, Alexandre

2008-01-01

This article presents the development of a system integrated to a ZigBee network to measure whole-body vibration. The developed system allows distinguishing human vibrations of almost 400Hz in three axes with acceleration of almost 50g. The tests conducted in the study ensured the correct functioning of the system for the project's purpose. PMID:27879866

Development of a Three Dimensional Wireless Sensor Network for Terrain-Climate Research in Remote Mountainous Environments

NASA Astrophysics Data System (ADS)

Kavanagh, K.; Davis, A.; Gessler, P.; Hess, H.; Holden, Z.; Link, T. E.; Newingham, B. A.; Smith, A. M.; Robinson, P.

2011-12-01

Developing sensor networks that are robust enough to perform in the world's remote regions is critical since these regions serve as important benchmarks compared to human-dominated areas. Paradoxically, the factors that make these remote, natural sites challenging for sensor networking are often what make them indispensable for climate change research. We aim to overcome these challenges by developing a three-dimensional sensor network arrayed across a topoclimatic gradient (1100-1800 meters) in a wilderness area in central Idaho. Development of this sensor array builds upon advances in sensing, networking, and power supply technologies coupled with experiences of the multidisciplinary investigators in conducting research in remote mountainous locations. The proposed gradient monitoring network will provide near real-time data from a three-dimensional (3-D) array of sensors measuring biophysical parameters used in ecosystem process models. The network will monitor atmospheric carbon dioxide concentration, humidity, air and soil temperature, soil water content, precipitation, incoming and outgoing shortwave and longwave radiation, snow depth, wind speed and direction, tree stem growth and leaf wetness at time intervals ranging from seconds to days. The long-term goal of this project is to realize a transformative integration of smart sensor networks adaptively communicating data in real-time to ultimately achieve a 3-D visualization of ecosystem processes within remote mountainous regions. Process models will be the interface between the visualization platforms and the sensor network. This will allow us to better predict how non-human dominated terrestrial and aquatic ecosystems function and respond to climate dynamics. Access to the data will be ensured as part of the Northwest Knowledge Network being developed at the University of Idaho, through ongoing Idaho NSF-funded cyber infrastructure initiatives, and existing data management systems funded by NSF, such as the CUAHSI Hydrologic Information System (HIS). These efforts will enhance cross-disciplinary understanding of natural and anthropogenic influences on ecosystem function and ultimately inform decision-making.

A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

2016-07-05

Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

Integrated expression analysis identifies transcription networks in mouse and human gastric neoplasia.

PubMed

Chen, Zheng; Soutto, Mohammed; Rahman, Bushra; Fazili, Muhammad W; Peng, DunFa; Blanca Piazuelo, Maria; Chen, Heidi; Kay Washington, M; Shyr, Yu; El-Rifai, Wael

2017-07-01

Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide. The Tff1 knockout (KO) mouse model develops gastric lesions that include low-grade dysplasia (LGD), high-grade dysplasia (HGD), and adenocarcinomas. In this study, we used Affymetrix microarrays gene expression platforms for analysis of molecular signatures in the mouse stomach [Tff1-KO (LGD) and Tff1 wild-type (normal)] and human gastric cancer tissues and their adjacent normal tissue samples. Combined integrated bioinformatics analysis of mouse and human datasets indicated that 172 genes were consistently deregulated in both human gastric cancer samples and Tff1-KO LGD lesions (P < .05). Using Ingenuity pathway analysis, these genes mapped to important transcription networks that include MYC, STAT3, β-catenin, RELA, NFATC2, HIF1A, and ETS1 in both human and mouse. Further analysis demonstrated activation of FOXM1 and inhibition of TP53 transcription networks in human gastric cancers but not in Tff1-KO LGD lesions. Using real-time RT-PCR, we validated the deregulated expression of several genes (VCAM1, BGN, CLDN2, COL1A1, COL1A2, COL3A1, EpCAM, IFITM1, MMP9, MMP12, MMP14, PDGFRB, PLAU, and TIMP1) that map to altered transcription networks in both mouse and human gastric neoplasia. Our study demonstrates significant similarities in deregulated transcription networks in human gastric cancer and gastric tumorigenesis in the Tff1-KO mouse model. The data also suggest that activation of MYC, STAT3, RELA, and β-catenin transcription networks could be an early molecular step in gastric carcinogenesis. © 2017 Wiley Periodicals, Inc.

Why Is Organizing Human Resource Development so Problematic? Perspectives from the Learning-Network Theory (Part I)

ERIC Educational Resources Information Center

Poell, Rob F.; van der Krogt, Ferd

2017-01-01

Purpose: Human resource development (HRD) is an important field within management. Developing employees is often regarded as an instrument to improve the internal labor market and support organizational change. Organizing HRD to these ends, however, is frequently a problematic affair, in terms of training effectiveness, participant motivation and…

Why is Organizing Human Resource Development so Problematic? Perspectives from the Learning-Network Theory (Part II)

ERIC Educational Resources Information Center

Poell, Rob F.; Van Der Krogt, Ferd

2017-01-01

Purpose: Human resource development (HRD) is an important field within management. Developing employees is often regarded as an instrument to improve the internal labor market and support organizational change. Organizing HRD to these ends, however, is frequently a problematic affair, in terms of training effectiveness, participant motivation and…

Is There a Mobile Social Presence?

ERIC Educational Resources Information Center

Tu, Chih-Hsiung; McIsaac, Marina; Sujo-Montes, Laura; Armfield, Shadow

2012-01-01

Mobile learning environments are human networks that afford the opportunity to participate in creative endeavors, social networking, organize/reorganize social contents, and manage social acts at anytime, anywhere through mobile technologies. Social acts that elicit identities, develop awareness, cement relationships, ensure connections, and…

Agricultural Trade Networks and Patterns of Economic Development

PubMed Central

Shutters, Shade T.; Muneepeerakul, Rachata

2012-01-01

International trade networks are manifestations of a complex combination of diverse underlying factors, both natural and social. Here we apply social network analytics to the international trade network of agricultural products to better understand the nature of this network and its relation to patterns of international development. Using a network tool known as triadic analysis we develop triad significance profiles for a series of agricultural commodities traded among countries. Results reveal a novel network “superfamily” combining properties of biological information processing networks and human social networks. To better understand this unique network signature, we examine in more detail the degree and triadic distributions within the trade network by country and commodity. Our results show that countries fall into two very distinct classes based on their triadic frequencies. Roughly 165 countries fall into one class while 18, all highly isolated with respect to international agricultural trade, fall into the other. Only Vietnam stands out as a unique case. Finally, we show that as a country becomes less isolated with respect to number of trading partners, the country's triadic signature follows a predictable trajectory that may correspond to a trajectory of development. PMID:22768310

Integrating Data and Networks: Human Factors

NASA Astrophysics Data System (ADS)

Chen, R. S.

2012-12-01

The development of technical linkages and interoperability between scientific networks is a necessary but not sufficient step towards integrated use and application of networked data and information for scientific and societal benefit. A range of "human factors" must also be addressed to ensure the long-term integration, sustainability, and utility of both the interoperable networks themselves and the scientific data and information to which they provide access. These human factors encompass the behavior of both individual humans and human institutions, and include system governance, a common framework for intellectual property rights and data sharing, consensus on terminology, metadata, and quality control processes, agreement on key system metrics and milestones, the compatibility of "business models" in the short and long term, harmonization of incentives for cooperation, and minimization of disincentives. Experience with several national and international initiatives and research programs such as the International Polar Year, the Group on Earth Observations, the NASA Earth Observing Data and Information System, the U.S. National Spatial Data Infrastructure, the Global Earthquake Model, and the United Nations Spatial Data Infrastructure provide a range of lessons regarding these human factors. Ongoing changes in science, technology, institutions, relationships, and even culture are creating both opportunities and challenges for expanded interoperability of scientific networks and significant improvement in data integration to advance science and the use of scientific data and information to achieve benefits for society as a whole.

The middleware architecture supports heterogeneous network systems for module-based personal robot system

NASA Astrophysics Data System (ADS)

Choo, Seongho; Li, Vitaly; Choi, Dong Hee; Jung, Gi Deck; Park, Hong Seong; Ryuh, Youngsun

2005-12-01

On developing the personal robot system presently, the internal architecture is every module those occupy separated functions are connected through heterogeneous network system. This module-based architecture supports specialization and division of labor at not only designing but also implementation, as an effect of this architecture, it can reduce developing times and costs for modules. Furthermore, because every module is connected among other modules through network systems, we can get easy integrations and synergy effect to apply advanced mutual functions by co-working some modules. In this architecture, one of the most important technologies is the network middleware that takes charge communications among each modules connected through heterogeneous networks systems. The network middleware acts as the human nerve system inside of personal robot system; it relays, transmits, and translates information appropriately between modules that are similar to human organizations. The network middleware supports various hardware platform, heterogeneous network systems (Ethernet, Wireless LAN, USB, IEEE 1394, CAN, CDMA-SMS, RS-232C). This paper discussed some mechanisms about our network middleware to intercommunication and routing among modules, methods for real-time data communication and fault-tolerant network service. There have designed and implemented a layered network middleware scheme, distributed routing management, network monitoring/notification technology on heterogeneous networks for these goals. The main theme is how to make routing information in our network middleware. Additionally, with this routing information table, we appended some features. Now we are designing, making a new version network middleware (we call 'OO M/W') that can support object-oriented operation, also are updating program sources itself for object-oriented architecture. It is lighter, faster, and can support more operation systems and heterogeneous network systems, but other general purposed middlewares like CORBA, UPnP, etc. can support only one network protocol or operating system.

Quasi-static modeling of human limb for intra-body communications with experiments.

PubMed

Pun, Sio Hang; Gao, Yue Ming; Mak, PengUn; Vai, Mang I; Du, Min

2011-11-01

In recent years, the increasing number of wearable devices on human has been witnessed as a trend. These devices can serve for many purposes: personal entertainment, communication, emergency mission, health care supervision, delivery, etc. Sharing information among the devices scattered across the human body requires a body area network (BAN) and body sensor network (BSN). However, implementation of the BAN/BSN with the conventional wireless technologies cannot give optimal result. It is mainly because the high requirements of light weight, miniature, energy efficiency, security, and less electromagnetic interference greatly limit the resources available for the communication modules. The newly developed intra-body communication (IBC) can alleviate most of the mentioned problems. This technique, which employs the human body as a communication channel, could be an innovative networking method for sensors and devices on the human body. In order to encourage the research and development of the IBC, the authors are favorable to lay a better and more formal theoretical foundation on IBC. They propose a multilayer mathematical model using volume conductor theory for galvanic coupling IBC on a human limb with consideration on the inhomogeneous properties of human tissue. By introducing and checking with quasi-static approximation criteria, Maxwell's equations are decoupled and capacitance effect is included to the governing equation for further improvement. Finally, the accuracy and potential of the model are examined from both in vitro and in vivo experimental results.

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers

PubMed Central

Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier

2017-01-01

Background The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. Objective MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. Methods MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. Results MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user’s specific interests and provides an efficient way to share information with collaborators. Furthermore, the user’s behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. Conclusions We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. PMID:28623182

Network integrity of the parental brain in infancy supports the development of children's social competencies.

PubMed

Abraham, Eyal; Hendler, Talma; Zagoory-Sharon, Orna; Feldman, Ruth

2016-11-01

The cross-generational transmission of mammalian sociality, initiated by the parent's postpartum brain plasticity and species-typical behavior that buttress offspring's socialization, has not been studied in humans. In this longitudinal study, we measured brain response of 45 primary-caregiving parents to their infant's stimuli, observed parent-infant interactions, and assayed parental oxytocin (OT). Intra- and inter-network connectivity were computed in three main networks of the human parental brain: core limbic, embodied simulation and mentalizing. During preschool, two key child social competencies were observed: emotion regulation and socialization. Parent's network integrity in infancy predicted preschoolers' social outcomes, with subcortical and cortical network integrity foreshadowing simple evolutionary-based regulatory tactics vs complex self-regulatory strategies and advanced socialization. Parent-infant synchrony mediated the links between connectivity of the parent's embodied simulation network and preschoolers' ability to use cognitive/executive emotion regulation strategies, highlighting the inherently dyadic nature of this network and its long-term effects on tuning young to social life. Parent's inter-network core limbic-embodied simulation connectivity predicted children's OT as moderated by parental OT. Findings challenge solipsistic neuroscience perspectives by demonstrating how the parent-offspring interface enables the brain of one human to profoundly impact long-term adaptation of another. © The Author (2016). Published by Oxford University Press.

Network integrity of the parental brain in infancy supports the development of children’s social competencies

PubMed Central

Abraham, Eyal; Hendler, Talma; Zagoory-Sharon, Orna

2016-01-01

The cross-generational transmission of mammalian sociality, initiated by the parent’s postpartum brain plasticity and species-typical behavior that buttress offspring’s socialization, has not been studied in humans. In this longitudinal study, we measured brain response of 45 primary-caregiving parents to their infant’s stimuli, observed parent–infant interactions, and assayed parental oxytocin (OT). Intra- and inter-network connectivity were computed in three main networks of the human parental brain: core limbic, embodied simulation and mentalizing. During preschool, two key child social competencies were observed: emotion regulation and socialization. Parent’s network integrity in infancy predicted preschoolers’ social outcomes, with subcortical and cortical network integrity foreshadowing simple evolutionary-based regulatory tactics vs complex self-regulatory strategies and advanced socialization. Parent–infant synchrony mediated the links between connectivity of the parent’s embodied simulation network and preschoolers' ability to use cognitive/executive emotion regulation strategies, highlighting the inherently dyadic nature of this network and its long-term effects on tuning young to social life. Parent’s inter-network core limbic-embodied simulation connectivity predicted children’s OT as moderated by parental OT. Findings challenge solipsistic neuroscience perspectives by demonstrating how the parent–offspring interface enables the brain of one human to profoundly impact long-term adaptation of another. PMID:27369068

Understanding the Construction of Personal Learning Networks to Support Non-Formal Workplace Learning of Training Professionals

ERIC Educational Resources Information Center

Manning, Christin

2013-01-01

Workers in the 21st century workplace are faced with rapid and constant developments that place a heavy demand on them to continually learn beyond what the Human Resources and Training groups can meet. As a consequence, professionals must rely on non-formal learning approaches through the development of a personal learning network to keep…

The Bacterial Mobile Resistome Transfer Network Connecting the Animal and Human Microbiomes

PubMed Central

Hu, Yongfei; Yang, Xi; Li, Jing; Lv, Na; Liu, Fei; Wu, Jun; Lin, Ivan Y. C.; Wu, Na; Gao, George F.

2016-01-01

ABSTRACT Horizontally acquired antibiotic resistance genes (ARGs) in bacteria are highly mobile and have been ranked as principal risk resistance determinants. However, the transfer network of the mobile resistome and the forces driving mobile ARG transfer are largely unknown. Here, we present the whole profile of the mobile resistome in 23,425 bacterial genomes and explore the effects of phylogeny and ecology on the recent transfer (≥99% nucleotide identity) of mobile ARGs. We found that mobile ARGs are mainly present in four bacterial phyla and are significantly enriched in Proteobacteria. The recent mobile ARG transfer network, which comprises 703 bacterial species and 16,859 species pairs, is shaped by the bacterial phylogeny, while an ecological barrier also exists, especially when interrogating bacteria colonizing different human body sites. Phylogeny is still a driving force for the transfer of mobile ARGs between farm animals and the human gut, and, interestingly, the mobile ARGs that are shared between the human and animal gut microbiomes are also harbored by diverse human pathogens. Taking these results together, we suggest that phylogeny and ecology are complementary in shaping the bacterial mobile resistome and exert synergistic effects on the development of antibiotic resistance in human pathogens. IMPORTANCE The development of antibiotic resistance threatens our modern medical achievements. The dissemination of antibiotic resistance can be largely attributed to the transfer of bacterial mobile antibiotic resistance genes (ARGs). Revealing the transfer network of these genes in bacteria and the forces driving the gene flow is of great importance for controlling and predicting the emergence of antibiotic resistance in the clinic. Here, by analyzing tens of thousands of bacterial genomes and millions of human and animal gut bacterial genes, we reveal that the transfer of mobile ARGs is mainly controlled by bacterial phylogeny but under ecological constraints. We also found that dozens of ARGs are transferred between the human and animal gut and human pathogens. This work demonstrates the whole profile of mobile ARGs and their transfer network in bacteria and provides further insight into the evolution and spread of antibiotic resistance in nature. PMID:27613679

The Bacterial Mobile Resistome Transfer Network Connecting the Animal and Human Microbiomes.

PubMed

Hu, Yongfei; Yang, Xi; Li, Jing; Lv, Na; Liu, Fei; Wu, Jun; Lin, Ivan Y C; Wu, Na; Weimer, Bart C; Gao, George F; Liu, Yulan; Zhu, Baoli

2016-11-15

Horizontally acquired antibiotic resistance genes (ARGs) in bacteria are highly mobile and have been ranked as principal risk resistance determinants. However, the transfer network of the mobile resistome and the forces driving mobile ARG transfer are largely unknown. Here, we present the whole profile of the mobile resistome in 23,425 bacterial genomes and explore the effects of phylogeny and ecology on the recent transfer (≥99% nucleotide identity) of mobile ARGs. We found that mobile ARGs are mainly present in four bacterial phyla and are significantly enriched in Proteobacteria The recent mobile ARG transfer network, which comprises 703 bacterial species and 16,859 species pairs, is shaped by the bacterial phylogeny, while an ecological barrier also exists, especially when interrogating bacteria colonizing different human body sites. Phylogeny is still a driving force for the transfer of mobile ARGs between farm animals and the human gut, and, interestingly, the mobile ARGs that are shared between the human and animal gut microbiomes are also harbored by diverse human pathogens. Taking these results together, we suggest that phylogeny and ecology are complementary in shaping the bacterial mobile resistome and exert synergistic effects on the development of antibiotic resistance in human pathogens. The development of antibiotic resistance threatens our modern medical achievements. The dissemination of antibiotic resistance can be largely attributed to the transfer of bacterial mobile antibiotic resistance genes (ARGs). Revealing the transfer network of these genes in bacteria and the forces driving the gene flow is of great importance for controlling and predicting the emergence of antibiotic resistance in the clinic. Here, by analyzing tens of thousands of bacterial genomes and millions of human and animal gut bacterial genes, we reveal that the transfer of mobile ARGs is mainly controlled by bacterial phylogeny but under ecological constraints. We also found that dozens of ARGs are transferred between the human and animal gut and human pathogens. This work demonstrates the whole profile of mobile ARGs and their transfer network in bacteria and provides further insight into the evolution and spread of antibiotic resistance in nature. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers

PubMed Central

Chen, Hao; Zhu, Zhitu; Zhu, Yichun; Wang, Jian; Mei, Yunqing; Cheng, Yunfeng

2015-01-01

It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network-based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein–protein or gene–gene interactions that can be monitored and evaluated at different stages and time-points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue-generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases-specific, stage-specific, severity-specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed. PMID:25560835

AC Electric Field Communication for Human-Area Networking

NASA Astrophysics Data System (ADS)

Kado, Yuichi; Shinagawa, Mitsuru

We have proposed a human-area networking technology that uses the surface of the human body as a data transmission path and uses an AC electric field signal below the resonant frequency of the human body. This technology aims to achieve a “touch and connect” intuitive form of communication by using the electric field signal that propagates along the surface of the human body, while suppressing both the electric field radiating from the human body and mutual interference. To suppress the radiation field, the frequency of the AC signal that excites the transmitter electrode must be lowered, and the sensitivity of the receiver must be raised while reducing transmission power to its minimally required level. We describe how we are developing AC electric field communication technologies to promote the further evolution of a human-area network in support of ubiquitous services, focusing on three main characteristics, enabling-transceiver technique, application-scenario modeling, and communications quality evaluation. Special attention is paid to the relationship between electro-magnetic compatibility evaluation and regulations for extremely low-power radio stations based on Japan's Radio Law.

Identification of a neuronal transcription factor network involved in medulloblastoma development.

PubMed

Lastowska, Maria; Al-Afghani, Hani; Al-Balool, Haya H; Sheth, Harsh; Mercer, Emma; Coxhead, Jonathan M; Redfern, Chris P F; Peters, Heiko; Burt, Alastair D; Santibanez-Koref, Mauro; Bacon, Chris M; Chesler, Louis; Rust, Alistair G; Adams, David J; Williamson, Daniel; Clifford, Steven C; Jackson, Michael S

2013-07-11

Medulloblastomas, the most frequent malignant brain tumours affecting children, comprise at least 4 distinct clinicogenetic subgroups. Aberrant sonic hedgehog (SHH) signalling is observed in approximately 25% of tumours and defines one subgroup. Although alterations in SHH pathway genes (e.g. PTCH1, SUFU) are observed in many of these tumours, high throughput genomic analyses have identified few other recurring mutations. Here, we have mutagenised the Ptch+/- murine tumour model using the Sleeping Beauty transposon system to identify additional genes and pathways involved in SHH subgroup medulloblastoma development. Mutagenesis significantly increased medulloblastoma frequency and identified 17 candidate cancer genes, including orthologs of genes somatically mutated (PTEN, CREBBP) or associated with poor outcome (PTEN, MYT1L) in the human disease. Strikingly, these candidate genes were enriched for transcription factors (p=2x10-5), the majority of which (6/7; Crebbp, Myt1L, Nfia, Nfib, Tead1 and Tgif2) were linked within a single regulatory network enriched for genes associated with a differentiated neuronal phenotype. Furthermore, activity of this network varied significantly between the human subgroups, was associated with metastatic disease, and predicted poor survival specifically within the SHH subgroup of tumours. Igf2, previously implicated in medulloblastoma, was the most differentially expressed gene in murine tumours with network perturbation, and network activity in both mouse and human tumours was characterised by enrichment for multiple gene-sets indicating increased cell proliferation, IGF signalling, MYC target upregulation, and decreased neuronal differentiation. Collectively, our data support a model of medulloblastoma development in SB-mutagenised Ptch+/- mice which involves disruption of a novel transcription factor network leading to Igf2 upregulation, proliferation of GNPs, and tumour formation. Moreover, our results identify rational therapeutic targets for SHH subgroup tumours, alongside prognostic biomarkers for the identification of poor-risk SHH patients.

Cyber situation awareness as distributed socio-cognitive work

NASA Astrophysics Data System (ADS)

Tyworth, Michael; Giacobe, Nicklaus A.; Mancuso, Vincent

2012-06-01

A key challenge for human cybersecurity operators is to develop an understanding of what is happening within, and to, their network. This understanding, or situation awareness, provides the cognitive basis for human operators to take action within their environments. Yet developing situation awareness of cyberspace (cyber-SA) is understood to be extremely difficult given the scope of the operating environment, the highly dynamic nature of the environment and the absence of physical constraints that serve to bound the cognitive task23. As a result, human cybersecurity operators are often "flying blind" regarding understanding the source, nature, and likely impact of malicious activity on their networked assets. In recent years, many scholars have dedicated their attention to finding ways to improve cyber-SA in human operators. In this paper we present our findings from our ongoing research of how cybersecurity analysts develop and maintain cyber-SA. Drawing from over twenty interviews of analysts working in the military, government, industrial, and educational domains, we find that cyber-SA to be distributed across human operators and technological artifacts operating in different functional areas.

Detection of Protein Complexes Based on Penalized Matrix Decomposition in a Sparse Protein⁻Protein Interaction Network.

PubMed

Cao, Buwen; Deng, Shuguang; Qin, Hua; Ding, Pingjian; Chen, Shaopeng; Li, Guanghui

2018-06-15

High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein⁻protein interaction (PPI) networks. In this study, based on penalized matrix decomposition ( PMD ), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMD pc ) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMD pc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR).

The UK Human Genome Mapping Project online computing service.

PubMed

Rysavy, F R; Bishop, M J; Gibbs, G P; Williams, G W

1992-04-01

This paper presents an overview of computing and networking facilities developed by the Medical Research Council to provide online computing support to the Human Genome Mapping Project (HGMP) in the UK. The facility is connected to a number of other computing facilities in various centres of genetics and molecular biology research excellence, either directly via high-speed links or through national and international wide-area networks. The paper describes the design and implementation of the current system, a 'client/server' network of Sun, IBM, DEC and Apple servers, gateways and workstations. A short outline of online computing services currently delivered by this system to the UK human genetics research community is also provided. More information about the services and their availability could be obtained by a direct approach to the UK HGMP-RC.

Human Rights Event Detection from Heterogeneous Social Media Graphs.

PubMed

Chen, Feng; Neill, Daniel B

2015-03-01

Human rights organizations are increasingly monitoring social media for identification, verification, and documentation of human rights violations. Since manual extraction of events from the massive amount of online social network data is difficult and time-consuming, we propose an approach for automated, large-scale discovery and analysis of human rights-related events. We apply our recently developed Non-Parametric Heterogeneous Graph Scan (NPHGS), which models social media data such as Twitter as a heterogeneous network (with multiple different node types, features, and relationships) and detects emerging patterns in the network, to identify and characterize human rights events. NPHGS efficiently maximizes a nonparametric scan statistic (an aggregate measure of anomalousness) over connected subgraphs of the heterogeneous network to identify the most anomalous network clusters. It summarizes each event with information such as type of event, geographical locations, time, and participants, and provides documentation such as links to videos and news reports. Building on our previous work that demonstrates the utility of NPHGS for civil unrest prediction and rare disease outbreak detection, we present an analysis of human rights events detected by NPHGS using two years of Twitter data from Mexico. NPHGS was able to accurately detect relevant clusters of human rights-related tweets prior to international news sources, and in some cases, prior to local news reports. Analysis of social media using NPHGS could enhance the information-gathering missions of human rights organizations by pinpointing specific abuses, revealing events and details that may be blocked from traditional media sources, and providing evidence of emerging patterns of human rights violations. This could lead to more timely, targeted, and effective advocacy, as well as other potential interventions.

OCT-based label-free in vivo lymphangiography within human skin and areola

NASA Astrophysics Data System (ADS)

Baran, Utku; Qin, Wan; Qi, Xiaoli; Kalkan, Goknur; Wang, Ruikang K.

2016-02-01

Due to the limitations of current imaging techniques, visualization of lymphatic capillaries within tissue in vivo has been challenging. Here, we present a label-free high resolution optical coherence tomography (OCT) based lymphangiography (OLAG) within human skin in vivo. OLAG enables rapid (~seconds) mapping of lymphatic networks, along with blood vessel networks, over 8 mm x 8 mm of human skin and 5 mm x 5 mm of human areola. Moreover, lymphatic system’s response to inflammation within human skin is monitored throughout an acne lesion development over 7 days. The demonstrated results promise OLAG as a revolutionary tool in the clinical research and treatment of patients with pathologic conditions such as cancer, diabetes, and autoimmune diseases.

Similarity or dissimilarity in the relations between human service organizations.

PubMed

Bruynooghe, Kevin; Verhaeghe, Mieke; Bracke, Piet

2008-01-01

Exchange theory and homophily theory give rise to counteracting expectations for the interaction between human service organizations. Based on arguments of exchange theory, more interaction is expected between dissimilar organizations having complementary resources. Based on arguments of homophily theory, organizations having similar characteristics are expected to interact more. Interorganizational relations between human service organizations in two regional networks in Flanders are examined in this study. Results indicate that human service organizations tend to cooperate more with similar organizations as several homophily effects but not one effect of dissimilarity were found to be significant. The results of this study contribute to the understanding of interorganizational networks of human service organizations and have implications for the development of integrated care.

Cross-population myelination covariance of human cerebral cortex.

PubMed

Ma, Zhiwei; Zhang, Nanyin

2017-09-01

Cross-population covariance of brain morphometric quantities provides a measure of interareal connectivity, as it is believed to be determined by the coordinated neurodevelopment of connected brain regions. Although useful, structural covariance analysis predominantly employed bulky morphological measures with mixed compartments, whereas studies of the structural covariance of any specific subdivisions such as myelin are rare. Characterizing myelination covariance is of interest, as it will reveal connectivity patterns determined by coordinated development of myeloarchitecture between brain regions. Using myelin content MRI maps from the Human Connectome Project, here we showed that the cortical myelination covariance was highly reproducible, and exhibited a brain organization similar to that previously revealed by other connectivity measures. Additionally, the myelination covariance network shared common topological features of human brain networks such as small-worldness. Furthermore, we found that the correlation between myelination covariance and resting-state functional connectivity (RSFC) was uniform within each resting-state network (RSN), but could considerably vary across RSNs. Interestingly, this myelination covariance-RSFC correlation was appreciably stronger in sensory and motor networks than cognitive and polymodal association networks, possibly due to their different circuitry structures. This study has established a new brain connectivity measure specifically related to axons, and this measure can be valuable to investigating coordinated myeloarchitecture development. Hum Brain Mapp 38:4730-4743, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Funding and Rationale for Early Intervention Services in Nebraska's "Early Development Network" in 2004: An Evaluation Study for the Nebraska Departments of Education and Health and Human Services. Final Report

ERIC Educational Resources Information Center

Marvin, Chris; Nugent, Gwen; Doll, Beth

2006-01-01

Anecdotal information has recently suggested that families of infants and toddlers with disabilities in Nebraska were seeking early intervention services from providers not affiliated with the free, state-sanctioned "Early Development Network" and children's "Individualized Family Service Plans" (IFSPs). The purpose of this…

Metals in the Human Environment Strategic Network (MITHE-SN): the interface of risk assessment, public policy, and advocacy.

PubMed

Hale, Beverley; Ritter, Len; Warner, Donna

2010-01-01

A 5-year strategic research network with a diverse base of industry, government, and academic partners was approved for support by National Sciences and Engineering Research Council of Canada (NSERC) on January 3, 2005. This Metals in the Human Environment Strategic Network (MITHE-SN) builds on, and further extends, science knowledge developed by the NSERC-sponsored Metals in the Environment Research Network (MITE-RN, 1999-2004). In addition to the initial award, the MITHE-SN received an additional 2-year grant specifically targeted to (1) enhance training opportunities for internships with international organizations, (2) increase international networking and linkages, and (3) optimize knowledge dissemination and technology transfer. The research program is comprised of three themes and represents a cascade of effects along food webs, from the lowest trophic levels to the highest consumers. Each of the themes addresses issues related to distinguishing the magnitudes and roles of natural background and anthropogenic metal inputs in biotic exposure to metals; estimating the bioavailable fraction of metals in the exposure media, thus better quantifying the true exposure concentration; and determining the factors that influence bioavailability of metals in media, so that predictive models can be developed for use in the development of site-specific metals criteria.

The Neonatal Connectome During Preterm Brain Development

PubMed Central

van den Heuvel, Martijn P.; Kersbergen, Karina J.; de Reus, Marcel A.; Keunen, Kristin; Kahn, René S.; Groenendaal, Floris; de Vries, Linda S.; Benders, Manon J.N.L.

2015-01-01

The human connectome is the result of an elaborate developmental trajectory. Acquiring diffusion-weighted imaging and resting-state fMRI, we studied connectome formation during the preterm phase of macroscopic connectome genesis. In total, 27 neonates were scanned at week 30 and/or week 40 gestational age (GA). Examining the architecture of the neonatal anatomical brain network revealed a clear presence of a small-world modular organization before term birth. Analysis of neonatal functional connectivity (FC) showed the early formation of resting-state networks, suggesting that functional networks are present in the preterm brain, albeit being in an immature state. Moreover, structural and FC patterns of the neonatal brain network showed strong overlap with connectome architecture of the adult brain (85 and 81%, respectively). Analysis of brain development between week 30 and week 40 GA revealed clear developmental effects in neonatal connectome architecture, including a significant increase in white matter microstructure (P < 0.01), small-world topology (P < 0.01) and interhemispheric FC (P < 0.01). Computational analysis further showed that developmental changes involved an increase in integration capacity of the connectivity network as a whole. Taken together, we conclude that hallmark organizational structures of the human connectome are present before term birth and subject to early development. PMID:24833018

25th anniversary article: The evolution of electronic skin (e-skin): a brief history, design considerations, and recent progress.

PubMed

Hammock, Mallory L; Chortos, Alex; Tee, Benjamin C-K; Tok, Jeffrey B-H; Bao, Zhenan

2013-11-13

Human skin is a remarkable organ. It consists of an integrated, stretchable network of sensors that relay information about tactile and thermal stimuli to the brain, allowing us to maneuver within our environment safely and effectively. Interest in large-area networks of electronic devices inspired by human skin is motivated by the promise of creating autonomous intelligent robots and biomimetic prosthetics, among other applications. The development of electronic networks comprised of flexible, stretchable, and robust devices that are compatible with large-area implementation and integrated with multiple functionalities is a testament to the progress in developing an electronic skin (e-skin) akin to human skin. E-skins are already capable of providing augmented performance over their organic counterpart, both in superior spatial resolution and thermal sensitivity. They could be further improved through the incorporation of additional functionalities (e.g., chemical and biological sensing) and desired properties (e.g., biodegradability and self-powering). Continued rapid progress in this area is promising for the development of a fully integrated e-skin in the near future. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Center for development technology and program in technology and human affairs. [emphasizing technology-based networks

NASA Technical Reports Server (NTRS)

Wong, M. D.

1974-01-01

The role of technology in nontraditional higher education with particular emphasis on technology-based networks is analyzed nontraditional programs, institutions, and consortia are briefly reviewed. Nontraditional programs which utilize technology are studied. Technology-based networks are surveyed and analyzed with regard to kinds of students, learning locations, technology utilization, interinstitutional relationships, cost aspects, problems, and future outlook.

Delay/Disruption Tolerant Networks for Human Space Flight Video Project

NASA Technical Reports Server (NTRS)

Fink, Patrick W.; Ngo, Phong; Schlesinger, Adam

2010-01-01

The movie describes collaboration between NASA and Vint Cerf on the development of Disruption Tolerant Networks (DTN) for use in space exploration. Current evaluation efforts at Johnson Space Center are focused on the use of DTNs in space communications. Tests include the ability of rovers to store data for later display, tracking local and remote habitat inventory using radio-frequency identification tags, and merging networks.

42 CFR 485.616 - Condition of participation: Agreements.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 5 2010-10-01 2010-10-01 false Condition of participation: Agreements. 485.616 Section 485.616 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN... development and use of communications systems of the network, including the network's system for the...

A modelling framework to evaluate human-induced alterations of network sediment connectivity and quantify their unplanned adverse impact

NASA Astrophysics Data System (ADS)

Bizzi, S.; Schmitt, R. J. P.; Giuliani, M.; Castelletti, A.

2016-12-01

World-wide human-induced alterations of sediment transport, e.g. due to dams, sand and gravel mining along rivers and channel maintenance, translated into geomorphic changes, which have had major effects on ecosystem integrity, human livelihoods, ultimately negatively impacting also on the expected benefit from building water infrastructures. Despite considerable recent advances in modelling basin-scale hydrological and geomorphological processes, our ability to quantitatively simulate network sediment transport, foresee effects of alternative scenarios of human development on fluvial morpho-dynamics, and design anticipatory planning adaptation measures is still limited. In this work, we demonstrate the potential of a novel modelling framework called CASCADE (CAtchment SEdiment Connectivity And Delivery (Schmitt et al., 2016)) to characterize sediment connectivity at the whole river network scale, predict the disturbing effect of dams on the sediment transport, and quantify the associated loss with respect to the level of benefits that provided the economic justification for their development. CASCADE allows tracking the fate of a sediment from its source to its multiple sinks across the network. We present the results from two major, transboundary river systems (3S and Red River) in South-East Asia. We first discuss the ability of CASCADE to properly represent sediment connectivity at the network scale using available remote sensing data and information from monitoring networks. Secondly, we assess the impacts on sediment connectivity induced by existing and planned dams in the 3S and Red River basins and compare these alterations with revenues in terms of hydropower production. CASCADE outputs support a broader understanding of sediment connectivity tailored for water management issues not yet available, and it is suitable to enrich assessments of food-energy-water nexus. The model framework can be embedded into the design of optimal siting and sizing of water infrastructures at the river basin scale. This enlarges the scope of the analysis to account for human-induced alterations of network sediment connectivity, and to explore the trade-off with respect to primary operational objectives, such as hydropower production, water supply, and flood control.

Development of distinct control networks through segregation and integration

PubMed Central

Fair, Damien A.; Dosenbach, Nico U. F.; Church, Jessica A.; Cohen, Alexander L.; Brahmbhatt, Shefali; Miezin, Francis M.; Barch, Deanna M.; Raichle, Marcus E.; Petersen, Steven E.; Schlaggar, Bradley L.

2007-01-01

Human attentional control is unrivaled. We recently proposed that adults depend on distinct frontoparietal and cinguloopercular networks for adaptive online task control versus more stable set control, respectively. During development, both experience-dependent evoked activity and spontaneous waves of synchronized cortical activity are thought to support the formation and maintenance of neural networks. Such mechanisms may encourage tighter “integration” of some regions into networks over time while “segregating” other sets of regions into separate networks. Here we use resting state functional connectivity MRI, which measures correlations in spontaneous blood oxygenation level-dependent signal fluctuations between brain regions to compare previously identified control networks between children and adults. We find that development of the proposed adult control networks involves both segregation (i.e., decreased short-range connections) and integration (i.e., increased long-range connections) of the brain regions that comprise them. Delay/disruption in the developmental processes of segregation and integration may play a role in disorders of control, such as autism, attention deficit hyperactivity disorder, and Tourette's syndrome. PMID:17679691

Dynamic phenomena and human activity in an artificial society

NASA Astrophysics Data System (ADS)

Grabowski, A.; Kruszewska, N.; Kosiński, R. A.

2008-12-01

We study dynamic phenomena in a large social network of nearly 3×104 individuals who interact in the large virtual world of a massive multiplayer online role playing game. On the basis of a database received from the online game server, we examine the structure of the friendship network and human dynamics. To investigate the relation between networks of acquaintances in virtual and real worlds, we carried out a survey among the players. We show that, even though the virtual network did not develop as a growing graph of an underlying network of social acquaintances in the real world, it influences it. Furthermore we find very interesting scaling laws concerning human dynamics. Our research shows how long people are interested in a single task and how much time they devote to it. Surprisingly, exponent values in both cases are close to -1 . We calculate the activity of individuals, i.e., the relative time daily devoted to interactions with others in the artificial society. Our research shows that the distribution of activity is not uniform and is highly correlated with the degree of the node, and that such human activity has a significant influence on dynamic phenomena, e.g., epidemic spreading and rumor propagation, in complex networks. We find that spreading is accelerated (an epidemic) or decelerated (a rumor) as a result of superspreaders’ various behavior.

Functional Interaction Network Construction and Analysis for Disease Discovery.

PubMed

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

Development of the social brain from age three to twelve years.

PubMed

Richardson, Hilary; Lisandrelli, Grace; Riobueno-Naylor, Alexa; Saxe, Rebecca

2018-03-12

Human adults recruit distinct networks of brain regions to think about the bodies and minds of others. This study characterizes the development of these networks, and tests for relationships between neural development and behavioral changes in reasoning about others' minds ('theory of mind', ToM). A large sample of children (n = 122, 3-12 years), and adults (n = 33), watched a short movie while undergoing fMRI. The movie highlights the characters' bodily sensations (often pain) and mental states (beliefs, desires, emotions), and is a feasible experiment for young children. Here we report three main findings: (1) ToM and pain networks are functionally distinct by age 3 years, (2) functional specialization increases throughout childhood, and (3) functional maturity of each network is related to increasingly anti-correlated responses between the networks. Furthermore, the most studied milestone in ToM development, passing explicit false-belief tasks, does not correspond to discontinuities in the development of the social brain.

A network engineering perspective on probing and perturbing cognition with neurofeedback

PubMed Central

Khambhati, Ankit N.

2017-01-01

Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition. PMID:28445589

A comparative study of theoretical graph models for characterizing structural networks of human brain.

PubMed

Li, Xiaojin; Hu, Xintao; Jin, Changfeng; Han, Junwei; Liu, Tianming; Guo, Lei; Hao, Wei; Li, Lingjiang

2013-01-01

Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

Extinction Dynamics and Control in Adaptive Networks

NASA Astrophysics Data System (ADS)

Schwartz, Ira; Shaw, Leah; Hindes, Jason

Disease control is of paramount importance in public health. Moreover, models of disease spread are an important component in implementing effective vaccination and treatment campaigns. However, human behavior in response to an outbreak has only recently been included in epidemic models on networks. Here we develop the mathematical machinery to describe the dynamics of extinction in finite populations that include human adaptive behavior. The formalism enables us to compute the optimal, fluctuation-induced path to extinction, and predict the average extinction time in adaptive networks as a function of the adaptation rate. We find that both observables have several unique scalings depending on the relative speed of infection and adaptivity. Finally, we discuss how the theory can be used to design optimal control programs in general networks, by coupling the effective force of noise with treatment and human behavior. Research supported by U.S. Naval Research Laboratory funding (Grant No. N0001414WX00023) and the Office of Naval Research (Grant No. N0001414WX20610).

The Development of Strategic Thinking: Learning to Impact Human Systems in a Youth Activism Program

ERIC Educational Resources Information Center

Larson, Reed; Hansen, David

2005-01-01

Human systems, including institutional systems and informal social networks, are a major arena of modern life. We argue that distinct forms of pragmatic reasoning or "strategic thinking" are required to exercise agency within such systems. This article explores the development of strategic thinking in a youth activism program in which young people…

Task-Related Edge Density (TED)—A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain

PubMed Central

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach “Task-related Edge Density” (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function. PMID:27341204

Task-Related Edge Density (TED)-A New Method for Revealing Dynamic Network Formation in fMRI Data of the Human Brain.

PubMed

Lohmann, Gabriele; Stelzer, Johannes; Zuber, Verena; Buschmann, Tilo; Margulies, Daniel; Bartels, Andreas; Scheffler, Klaus

2016-01-01

The formation of transient networks in response to external stimuli or as a reflection of internal cognitive processes is a hallmark of human brain function. However, its identification in fMRI data of the human brain is notoriously difficult. Here we propose a new method of fMRI data analysis that tackles this problem by considering large-scale, task-related synchronisation networks. Networks consist of nodes and edges connecting them, where nodes correspond to voxels in fMRI data, and the weight of an edge is determined via task-related changes in dynamic synchronisation between their respective times series. Based on these definitions, we developed a new data analysis algorithm that identifies edges that show differing levels of synchrony between two distinct task conditions and that occur in dense packs with similar characteristics. Hence, we call this approach "Task-related Edge Density" (TED). TED proved to be a very strong marker for dynamic network formation that easily lends itself to statistical analysis using large scale statistical inference. A major advantage of TED compared to other methods is that it does not depend on any specific hemodynamic response model, and it also does not require a presegmentation of the data for dimensionality reduction as it can handle large networks consisting of tens of thousands of voxels. We applied TED to fMRI data of a fingertapping and an emotion processing task provided by the Human Connectome Project. TED revealed network-based involvement of a large number of brain areas that evaded detection using traditional GLM-based analysis. We show that our proposed method provides an entirely new window into the immense complexity of human brain function.

New patterns in human biogeography revealed by networks of contacts between linguistic groups.

PubMed

Capitán, José A; Bock Axelsen, Jacob; Manrubia, Susanna

2015-03-07

Human languages differ broadly in abundance and are distributed highly unevenly on the Earth. In many qualitative and quantitative aspects, they strongly resemble biodiversity distributions. An intriguing and previously unexplored issue is the architecture of the neighbouring relationships between human linguistic groups. Here we construct and characterize these networks of contacts and show that they represent a new kind of spatial network with uncommon structural properties. Remarkably, language networks share a meaningful property with food webs: both are quasi-interval graphs. In food webs, intervality is linked to the existence of a niche space of low dimensionality; in language networks, we show that the unique relevant variable is the area occupied by the speakers of a language. By means of a range model analogous to niche models in ecology, we show that a geometric restriction of perimeter covering by neighbouring linguistic domains explains the structural patterns observed. Our findings may be of interest in the development of models for language dynamics or regarding the propagation of cultural innovations. In relation to species distribution, they pose the question of whether the spatial features of species ranges share architecture, and eventually generating mechanism, with the distribution of human linguistic groups. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Robust network design for multispecies conservation

Treesearch

Ronan Le Bras; Bistra Dilkina; Yexiang Xue; Carla P. Gomes; Kevin S. McKelvey; Michael K. Schwartz; Claire A. Montgomery

2013-01-01

Our work is motivated by an important network design application in computational sustainability concerning wildlife conservation. In the face of human development and climate change, it is important that conservation plans for protecting landscape connectivity exhibit certain level of robustness. While previous work has focused on conservation strategies that result...

Applications of spatial statistical network models to stream data

Treesearch

Daniel J. Isaak; Erin E. Peterson; Jay M. Ver Hoef; Seth J. Wenger; Jeffrey A. Falke; Christian E. Torgersen; Colin Sowder; E. Ashley Steel; Marie-Josee Fortin; Chris E. Jordan; Aaron S. Ruesch; Nicholas Som; Pascal Monestiez

2014-01-01

Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for...

Issues of Gender. Symposium.

ERIC Educational Resources Information Center

2002

This symposium is comprised of three papers on issues of gender in human resource development (HRD). "The Impact of Awareness and Action on the Implementation of a Women's Network" (Laura L. Bierema) reports on research to examine how gender consciousness emerges through the formation of in-company networks to promote corporate women's…

Developing and applying a gene functional association network for anti-angiogenic kinase inhibitor activity assessment in an angiogenesis co-culture model

PubMed Central

Chen, Yuefeng; Wei, Tao; Yan, Lei; Lawrence, Frank; Qian, Hui-Rong; Burkholder, Timothy P; Starling, James J; Yingling, Jonathan M; Shou, Jianyong

2008-01-01

Background Tumor angiogenesis is a highly regulated process involving intercellular communication as well as the interactions of multiple downstream signal transduction pathways. Disrupting one or even a few angiogenesis pathways is often insufficient to achieve sustained therapeutic benefits due to the complexity of angiogenesis. Targeting multiple angiogenic pathways has been increasingly recognized as a viable strategy. However, translation of the polypharmacology of a given compound to its antiangiogenic efficacy remains a major technical challenge. Developing a global functional association network among angiogenesis-related genes is much needed to facilitate holistic understanding of angiogenesis and to aid the development of more effective anti-angiogenesis therapeutics. Results We constructed a comprehensive gene functional association network or interactome by transcript profiling an in vitro angiogenesis model, in which human umbilical vein endothelial cells (HUVECs) formed capillary structures when co-cultured with normal human dermal fibroblasts (NHDFs). HUVEC competence and NHDF supportiveness of cord formation were found to be highly cell-passage dependent. An enrichment test of Biological Processes (BP) of differentially expressed genes (DEG) revealed that angiogenesis related BP categories significantly changed with cell passages. Built upon 2012 DEGs identified from two microarray studies, the resulting interactome captured 17226 functional gene associations and displayed characteristics of a scale-free network. The interactome includes the involvement of oncogenes and tumor suppressor genes in angiogenesis. We developed a network walking algorithm to extract connectivity information from the interactome and applied it to simulate the level of network perturbation by three multi-targeted anti-angiogenic kinase inhibitors. Simulated network perturbation correlated with observed anti-angiogenesis activity in a cord formation bioassay. Conclusion We established a comprehensive gene functional association network to model in vitro angiogenesis regulation. The present study provided a proof-of-concept pilot of applying network perturbation analysis to drug phenotypic activity assessment. PMID:18518970

Multilayer motif analysis of brain networks

NASA Astrophysics Data System (ADS)

Battiston, Federico; Nicosia, Vincenzo; Chavez, Mario; Latora, Vito

2017-04-01

In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.

Structural Organization of the Laryngeal Motor Cortical Network and Its Implication for Evolution of Speech Production.

PubMed

Kumar, Veena; Croxson, Paula L; Simonyan, Kristina

2016-04-13

The laryngeal motor cortex (LMC) is essential for the production of learned vocal behaviors because bilateral damage to this area renders humans unable to speak but has no apparent effect on innate vocalizations such as human laughing and crying or monkey calls. Several hypotheses have been put forward attempting to explain the evolutionary changes from monkeys to humans that potentially led to enhanced LMC functionality for finer motor control of speech production. These views, however, remain limited to the position of the larynx area within the motor cortex, as well as its connections with the phonatory brainstem regions responsible for the direct control of laryngeal muscles. Using probabilistic diffusion tractography in healthy humans and rhesus monkeys, we show that, whereas the LMC structural network is largely comparable in both species, the LMC establishes nearly 7-fold stronger connectivity with the somatosensory and inferior parietal cortices in humans than in macaques. These findings suggest that important "hard-wired" components of the human LMC network controlling the laryngeal component of speech motor output evolved from an already existing, similar network in nonhuman primates. However, the evolution of enhanced LMC-parietal connections likely allowed for more complex synchrony of higher-order sensorimotor coordination, proprioceptive and tactile feedback, and modulation of learned voice for speech production. The role of the primary motor cortex in the formation of a comprehensive network controlling speech and language has been long underestimated and poorly studied. Here, we provide comparative and quantitative evidence for the significance of this region in the control of a highly learned and uniquely human behavior: speech production. From the viewpoint of structural network organization, we discuss potential evolutionary advances of enhanced temporoparietal cortical connections with the laryngeal motor cortex in humans compared with nonhuman primates that may have contributed to the development of finer vocal motor control necessary for speech production. Copyright © 2016 the authors 0270-6474/16/364170-12$15.00/0.

Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks.

PubMed

Balaur, Irina; Mazein, Alexander; Saqi, Mansoor; Lysenko, Artem; Rawlings, Christopher J; Auffray, Charles

2017-04-01

The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/ . The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework . ibalaur@eisbm.org. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks

PubMed Central

Mazein, Alexander; Saqi, Mansoor; Lysenko, Artem; Rawlings, Christopher J.; Auffray, Charles

2017-01-01

Abstract Summary: The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. Availability and Implementation: The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/. The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework. Contact: ibalaur@eisbm.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27993779

Building the South Carolina Network for Educational Telecomputing. Four Years of Growth and Success--Bringing Educators Together. Annual Report to BellSouth Foundation. Project REACH, Telecommunications Grant (1 July 1992-1 April 1993).

ERIC Educational Resources Information Center

Oliver, S. Kemble, III

Expanding on its original purpose in developing an electronic community of humanities teachers and students in South Carolina from an existing agricultural telecommunications system, Project REACH received authorization from its sponsor, the BellSouth Foundation, to develop a statewide network for educational telecommunications and to provide…

Education and Human Rights Violation in Guyana. Texas Papers on Latin America. Pre-publication Working Papers of the Institute of Latin American Studies, University of Texas at Austin. Paper No. 90-03.

ERIC Educational Resources Information Center

Samaroo, Noel K.

Education plays a central role in contemporary social development and change. The educational system in a given society assumes a major role in human development by making available to the individual the necessary equipment for interfacing with the network of social relations. In both developed and developing countries, education increasingly has…

Modelling the effect of religion on human empathy based on an adaptive temporal-causal network model.

PubMed

van Ments, Laila; Roelofsma, Peter; Treur, Jan

2018-01-01

Religion is a central aspect of many individuals' lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. By first developing a conceptual representation of a network model based on the literature, and then formalizing this model into a numerical representation, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus was mainly on the influence of religion on human empathy and dis-empathy, a topic very relevant today. The developed model could be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals. It is illustrated for a number of different scenarios based on different characteristics of the persons and of the religion.

Tutorial: Neural networks and their potential application in nuclear power plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Uhrig, R.E.

A neural network is a data processing system consisting of a number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Examples include reading Japanese Kanjimore » characters and human handwriting, reading a typewritten manuscript aloud, compensating for alignment errors in robots, interpreting very noise'' signals (e.g. electroencephalograms), modeling complex systems that cannot be modelled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and describes research on the potential applications to nuclear power plants.« less

A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology.

PubMed

Bahramali, Golnaz; Goliaei, Bahram; Minuchehr, Zarrin; Marashi, Sayed-Amir

2017-02-01

Chameleon proteins are proteins which include sequences that can adopt α-helix-β-strand (HE-chameleon) or α-helix-coil (HC-chameleon) or β-strand-coil (CE-chameleon) structures to operate their crucial biological functions. In this study, using a network-based approach, we examined the chameleon proteins to give a better knowledge on these proteins. We focused on proteins with identical chameleon sequences with more than or equal to seven residues long in different PDB entries, which adopt HE-chameleon, HC-chameleon, and CE-chameleon structures in the same protein. One hundred and ninety-one human chameleon proteins were identified via our in-house program. Then, protein-protein interaction (PPI) networks, Gene ontology (GO) enrichment, disease network, and pathway enrichment analyses were performed for our derived data set. We discovered that there are chameleon sequences which reside in protein-protein interaction regions between two proteins critical for their dual function. Analysis of the PPI networks for chameleon proteins introduced five hub proteins, namely TP53, EGFR, HSP90AA1, PPARA, and HIF1A, which were presented in four PPI clusters. The outcomes demonstrate that the chameleon regions are in critical domains of these proteins and are important in the development and treatment of human cancers. The present report is the first network-based functional study of chameleon proteins using computational approaches and might provide a new perspective for understanding the mechanisms of diseases helping us in developing new medical therapies along with discovering new proteins with chameleon properties which are highly important in cancer.

SEEMP: A Networked Marketplace for Employment Services

NASA Astrophysics Data System (ADS)

Celino, Irene; Cerizza, Dario; Cesarini, Mirko; Valle, Emanuele Della; de Paoli, Flavio; Estublier, Jacky; Fugini, Mariagrazia; Pérez, Asuncion Gómez; Kerrigan, Mick; Guarrera, Pascal; Mezzanzanica, Mario; Ramìrez, Jaime; Villazon, Boris; Zhao, Gang

Human capital is more and more the key factor of economic growth and competitiveness in the information age and knowledge economy. But due to a still fragmented employment market compounded by the enlargement of the EU, the human resources are not effectively exchanged and deployed. The business innovation of SEEMP1 develops a vision of an Employment Mediation Marketplace (EMM) for market transparency and effic ient mediation. Its technological innovation provides a federated marketplace of employment agencies through a peer-to-peer network of employment data and mediation services. In other words, the solution under development is a de-fragmentation of the employment market by a web-based collaborative network. The SEEMP-enabled employment marketplace will strengthen the social organization of public employment administration, maximize the business turnover of private employment agencies, improve citizens' productivity and welfare, and increase the competitiveness and performance of business.

A Dynamic Network Model to Explain the Development of Excellent Human Performance

PubMed Central

Den Hartigh, Ruud J. R.; Van Dijk, Marijn W. G.; Steenbeek, Henderien W.; Van Geert, Paul L. C.

2016-01-01

Across different domains, from sports to science, some individuals accomplish excellent levels of performance. For over 150 years, researchers have debated the roles of specific nature and nurture components to develop excellence. In this article, we argue that the key to excellence does not reside in specific underlying components, but rather in the ongoing interactions among the components. We propose that excellence emerges out of dynamic networks consisting of idiosyncratic mixtures of interacting components such as genetic endowment, motivation, practice, and coaching. Using computer simulations we demonstrate that the dynamic network model accurately predicts typical properties of excellence reported in the literature, such as the idiosyncratic developmental trajectories leading to excellence and the highly skewed distributions of productivity present in virtually any achievement domain. Based on this novel theoretical perspective on excellent human performance, this article concludes by suggesting policy implications and directions for future research. PMID:27148140

PyPathway: Python Package for Biological Network Analysis and Visualization.

PubMed

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

Functional Network Development During the First Year: Relative Sequence and Socioeconomic Correlations

PubMed Central

Gao, Wei; Alcauter, Sarael; Elton, Amanda; Hernandez-Castillo, Carlos R.; Smith, J. Keith; Ramirez, Juanita; Lin, Weili

2015-01-01

The first postnatal year is characterized by the most dramatic functional network development of the human lifespan. Yet, the relative sequence of the maturation of different networks and the impact of socioeconomic status (SES) on their development during this critical period remains poorly characterized. Leveraging a large, normally developing infant sample with multiple longitudinal resting-state functional magnetic resonance imaging scans during the first year (N = 65, scanned every 3 months), we aimed to delineate the relative maturation sequence of 9 key brain functional networks and examine their SES correlations. Our results revealed a maturation sequence from primary sensorimotor/auditory to visual to attention/default-mode, and finally to executive control networks. Network-specific critical growth periods were also identified. Finally, marginally significant positive SES–brain correlations were observed at 6 months of age for both the sensorimotor and default-mode networks, indicating interesting SES effects on functional brain maturation. To the best of our knowledge, this is the first study delineating detailed longitudinal growth trajectories of all major functional networks during the first year of life and their SES correlations. Insights from this study not only improve our understanding of early brain development, but may also inform the critical periods for SES expression during infancy. PMID:24812084

A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology

PubMed Central

2011-01-01

Background Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown utility in the study of systems medicine. However, no integrated analysis between human tissues has been done. Results To describe tissue-specific functions, Recon 1 was tailored to describe metabolism in three human cells: adipocytes, hepatocytes, and myocytes. These cell-specific networks were manually curated and validated based on known cellular metabolic functions. To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles. In addition, the multi-tissue model was used to study diabetes: a pathology with systemic properties. High-throughput data was integrated with the network to determine differential metabolic activity between obese and type II obese gastric bypass patients in a whole-body context. Conclusion The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states. As more cell and tissue-specific models are released, it is critical to develop a framework in which to study their interdependencies. PMID:22041191

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis.

PubMed

Luo, Heng; Ye, Hao; Ng, Hui; Shi, Leming; Tong, Weida; Mattes, William; Mendrick, Donna; Hong, Huixiao

2015-01-01

As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.

Understanding and predicting binding between human leukocyte antigens (HLAs) and peptides by network analysis

PubMed Central

2015-01-01

Background As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding. Methods Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network. Results Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature. Conclusions Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs. PMID:26424483

Human Behavior Modeling in Network Science

DTIC Science & Technology

2010-03-01

in Network Science bringing three distinct research areas together, communication networks, information networks, and social /cognitive networks. The...researchers. A critical part of the social /cognitive network effort is the modeling of human behavior. The modeling efforts range from organizational...behavior to social cognitive trust to explore and refine the theoretical and applied network relationships between and among the human

Sensory grammars for sensor networks

PubMed Central

Aloimonos, Yiannis

2009-01-01

One of the major goals of Ambient Intelligence and Smart Environments is to interpret human activity sensed by a variety of sensors. In order to develop useful technologies and a subsequent industry around smart environments, we need to proceed in a principled manner. This paper suggests that human activity can be expressed in a language. This is a special language with its own phonemes, its own morphemes (words) and its own syntax and it can be learned using machine learning techniques applied to gargantuan amounts of data collected by sensor networks. Developing such languages will create bridges between Ambient Intelligence and other disciplines. It will also provide a hierarchical structure that can lead to a successful industry. PMID:21897837

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

PubMed

Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

2017-06-16

The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. ©Alexis Allot, Kirsley Chennen, Yannis Nevers, Laetitia Poidevin, Arnaud Kress, Raymond Ripp, Julie Dawn Thompson, Olivier Poch, Odile Lecompte. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.06.2017.

SIMULATING FISH ASSEMBLAGE DYNAMICS IN RIVER NETWORKS

EPA Science Inventory

My recently retired colleague, Joan Baker, and I have developed a prototype computer simulation model for studying the effects of human and non-human alterations of habitats and species availability on fish assemblage populations. The fish assemblage model, written in R, is a sp...

A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human.

PubMed

Vo, Tommy V; Das, Jishnu; Meyer, Michael J; Cordero, Nicolas A; Akturk, Nurten; Wei, Xiaomu; Fair, Benjamin J; Degatano, Andrew G; Fragoza, Robert; Liu, Lisa G; Matsuyama, Akihisa; Trickey, Michelle; Horibata, Sachi; Grimson, Andrew; Yamano, Hiroyuki; Yoshida, Minoru; Roth, Frederick P; Pleiss, Jeffrey A; Xia, Yu; Yu, Haiyuan

2016-01-14

Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ∼ 50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution. Copyright © 2016 Elsevier Inc. All rights reserved.

Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness

PubMed Central

Kim, Minkyung; Mashour, George A.; Moraes, Stefanie-Blain; Vanini, Giancarlo; Tarnal, Vijay; Janke, Ellen; Hudetz, Anthony G.; Lee, Uncheol

2016-01-01

Sleep, anesthesia, and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four variables that are involved in facilitating ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states. PMID:26834616

Functional and Topological Conditions for Explosive Synchronization Develop in Human Brain Networks with the Onset of Anesthetic-Induced Unconsciousness.

PubMed

Kim, Minkyung; Mashour, George A; Moraes, Stefanie-Blain; Vanini, Giancarlo; Tarnal, Vijay; Janke, Ellen; Hudetz, Anthony G; Lee, Uncheol

2016-01-01

Sleep, anesthesia, and coma share a number of neural features but the recovery profiles are radically different. To understand the mechanisms of reversibility of unconsciousness at the network level, we studied the conditions for gradual and abrupt transitions in conscious and anesthetized states. We hypothesized that the conditions for explosive synchronization (ES) in human brain networks would be present in the anesthetized brain just over the threshold of unconsciousness. To test this hypothesis, functional brain networks were constructed from multi-channel electroencephalogram (EEG) recordings in seven healthy subjects across conscious, unconscious, and recovery states. We analyzed four variables that are involved in facilitating ES in generic, non-biological networks: (1) correlation between node degree and frequency, (2) disassortativity (i.e., the tendency of highly-connected nodes to link with less-connected nodes, or vice versa), (3) frequency difference of coupled nodes, and (4) an inequality relationship between local and global network properties, which is referred to as the suppressive rule. We observed that the four network conditions for ES were satisfied in the unconscious state. Conditions for ES in the human brain suggest a potential mechanism for rapid recovery from the lightly-anesthetized state. This study demonstrates for the first time that the network conditions for ES, formerly shown in generic networks only, are present in empirically-derived functional brain networks. Further investigations with deep anesthesia, sleep, and coma could provide insight into the underlying causes of variability in recovery profiles of these unconscious states.

Simulating Autonomous Telecommunication Networks for Space Exploration

NASA Technical Reports Server (NTRS)

Segui, John S.; Jennings, Esther H.

2008-01-01

Currently, most interplanetary telecommunication systems require human intervention for command and control. However, considering the range from near Earth to deep space missions, combined with the increase in the number of nodes and advancements in processing capabilities, the benefits from communication autonomy will be immense. Likewise, greater mission science autonomy brings the need for unscheduled, unpredictable communication and network routing. While the terrestrial Internet protocols are highly developed their suitability for space exploration has been questioned. JPL has developed the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) tool to help characterize network designs and protocols. The results will allow future mission planners to better understand the trade offs of communication protocols. This paper discusses various issues with interplanetary network and simulation results of interplanetary networking protocols.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations.

PubMed

Perez-Lopez, Áron R; Szalay, Kristóf Z; Türei, Dénes; Módos, Dezső; Lenti, Katalin; Korcsmáros, Tamás; Csermely, Peter

2015-05-11

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

NASA Astrophysics Data System (ADS)

Perez-Lopez, Áron R.; Szalay, Kristóf Z.; Türei, Dénes; Módos, Dezső; Lenti, Katalin; Korcsmáros, Tamás; Csermely, Peter

2015-05-01

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

PubMed Central

Perez-Lopez, Áron R.; Szalay, Kristóf Z.; Türei, Dénes; Módos, Dezső; Lenti, Katalin; Korcsmáros, Tamás; Csermely, Peter

2015-01-01

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates. PMID:25960144

The Topology of the Growing Human Interactome Data.

PubMed

Janjić, Vuk; Pržulj, Nataša

2014-06-01

We have long moved past the one-gene-one-function concept originally proposed by Beadle and Tatum back in 1941; but the full understanding of genotype-phenotype relations still largely relies on the analysis of static, snapshot-like, interaction data sets. Here, we look at what global patterns can be uncovered if we simply trace back the human interactome network over the last decade of protein-protein interaction (PPI) screening. We take a purely topological approach and find that as the human interactome is getting denser, it is not only gaining in structure (in terms of now being better fit by structured network models than before), but also there are patterns in the way in which it is growing: (a) newly added proteins tend to get linked to existing proteins in the interactome that are not know to interact; and (b) new proteins tend to link to already well connected proteins. Moreover, the alignment between human and yeast interactomes spanning over 40% of yeast's proteins - that are involved in regulation of transcription, RNA splicing and other cellcycle- related processes-suggests the existence of a part of the interactome which remains topologically and functionally unaffected through evolution. Furthermore, we find a small sub-network, specific to the "core" of the human interactome and involved in regulation of transcription and cancer development, whose wiring has not changed within the human interactome over the last 10 years of interacome data acquisition. Finally, we introduce a generalisation of the clustering coefficient of a network as a new measure called the cycle coefficient, and use it to show that PPI networks of human and model organisms are wired in a tight way which forbids the occurrence large cycles.

The topology of the growing human interactome data.

PubMed

Janjić, Vuk; Pržulj, Nataša

2014-06-23

We have long moved past the one-gene–one-function concept originally proposed by Beadle and Tatum back in 1941; but the full understanding of genotype–phenotype relations still largely relies on the analysis of static, snapshot-like, interaction data sets. Here, we look at what global patterns can be uncovered if we simply trace back the human interactome network over the last decade of protein- protein interaction (PPI) screening. We take a purely topological approach and find that as the human interactome is getting denser, it is not only gaining in structure (in terms of now being better fit by structured network models than before), but also there are patterns in the way in which it is growing: (a) newly added proteins tend to get linked to existing proteins in the interactome that are not know to interact; and (b) new proteins tend to link to already well connected proteins. Moreover, the alignment between human and yeast interactomes spanning over 40% of yeast’s proteins — that are involved in regulation of transcription, RNA splicing and other cellcycle-related processes—suggests the existence of a part of the interactome which remains topologically and functionally unaffected through evolution. Furthermore, we find a small sub-network, specific to the “core” of the human interactome and involved in regulation of transcription and cancer development, whose wiring has not changed within the human interactome over the last 10 years of interacome data acquisition. Finally, we introduce a generalisation of the clustering coefficient of a network as a new measure called the cycle coefficient, and use it to show that PPI networks of human and model organisms are wired in a tight way which forbids the occurrence large cycles.

Diversified Control Paths: A Significant Way Disease Genes Perturb the Human Regulatory Network

PubMed Central

Wang, Bingbo; Gao, Lin; Zhang, Qingfang; Li, Aimin; Deng, Yue; Guo, Xingli

2015-01-01

Background The complexity of biological systems motivates us to use the underlying networks to provide deep understanding of disease etiology and the human diseases are viewed as perturbations of dynamic properties of networks. Control theory that deals with dynamic systems has been successfully used to capture systems-level knowledge in large amount of quantitative biological interactions. But from the perspective of system control, the ways by which multiple genetic factors jointly perturb a disease phenotype still remain. Results In this work, we combine tools from control theory and network science to address the diversified control paths in complex networks. Then the ways by which the disease genes perturb biological systems are identified and quantified by the control paths in a human regulatory network. Furthermore, as an application, prioritization of candidate genes is presented by use of control path analysis and gene ontology annotation for definition of similarities. We use leave-one-out cross-validation to evaluate the ability of finding the gene-disease relationship. Results have shown compatible performance with previous sophisticated works, especially in directed systems. Conclusions Our results inspire a deeper understanding of molecular mechanisms that drive pathological processes. Diversified control paths offer a basis for integrated intervention techniques which will ultimately lead to the development of novel therapeutic strategies. PMID:26284649

The Potential of the Nutrient Uptake and Outcome network (NUOnet) to Contribute to Soil and Water Conservation

USDA-ARS?s Scientific Manuscript database

With the national and global environmental challenges that we have related to nutrient management, there is a need to use large quantities of information to solve the complex agricultural challenges humanity faces. USDA-ARS is developing a national network called the Nutrient Uptake and Outcome netw...

Social contagion theory: examining dynamic social networks and human behavior

PubMed Central

Christakis, Nicholas A.; Fowler, James H.

2013-01-01

Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a ‘three degrees of influence’ property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations. PMID:22711416

Network-based study reveals potential infection pathways of hepatitis-C leading to various diseases.

PubMed

Mukhopadhyay, Anirban; Maulik, Ujjwal

2014-01-01

Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement.

Network-Based Study Reveals Potential Infection Pathways of Hepatitis-C Leading to Various Diseases

PubMed Central

Mukhopadhyay, Anirban; Maulik, Ujjwal

2014-01-01

Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement. PMID:24743187

modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks.

PubMed

Sain, Neetu; Mohanty, Debasisa

2016-09-21

PDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification of PDZ interaction networks. Currently available tools for prediction of interaction partners of PDZ domains utilize machine learning approach. Since, they have been trained using experimental substrate specificity data for specific PDZ families, their applicability is limited to PDZ families closely related to the training set. These tools also do not allow analysis of PDZ-peptide interaction interfaces. We have used a structure based approach to develop modPDZpep, a program to predict the interaction partners of human PDZ domains and analyze structural details of PDZ interaction interfaces. modPDZpep predicts interaction partners by using structural models of PDZ-peptide complexes and evaluating binding energy scores using residue based statistical pair potentials. Since, it does not require training using experimental data on peptide binding affinity, it can predict substrates for diverse PDZ families. Because of the use of simple scoring function for binding energy, it is also fast enough for genome scale structure based analysis of PDZ interaction networks. Benchmarking using artificial as well as real negative datasets indicates good predictive power with ROC-AUC values in the range of 0.7 to 0.9 for a large number of human PDZ domains. Another novel feature of modPDZpep is its ability to map novel PDZ mediated interactions in human protein-protein interaction networks, either by utilizing available experimental phage display data or by structure based predictions. In summary, we have developed modPDZpep, a web-server for structure based analysis of human PDZ domains. It is freely available at http://www.nii.ac.in/modPDZpep.html or http://202.54.226.235/modPDZpep.html . This article was reviewed by Michael Gromiha and Zoltán Gáspári.

Humans use compression heuristics to improve the recall of social networks.

PubMed

Brashears, Matthew E

2013-01-01

The ability of primates, including humans, to maintain large social networks appears to depend on the ratio of the neocortex to the rest of the brain. However, observed human network size frequently exceeds predictions based on this ratio (e.g., "Dunbar's Number"), implying that human networks are too large to be cognitively managed. Here I show that humans adaptively use compression heuristics to allow larger amounts of social information to be stored in the same brain volume. I find that human adults can remember larger numbers of relationships in greater detail when a network exhibits triadic closure and kin labels than when it does not. These findings help to explain how humans manage large and complex social networks with finite cognitive resources and suggest that many of the unusual properties of human social networks are rooted in the strategies necessary to cope with cognitive limitations.

Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.

PubMed

Xiao, Xiaolin; Moreno-Moral, Aida; Rotival, Maxime; Bottolo, Leonardo; Petretto, Enrico

2014-01-01

Recent high-throughput efforts such as ENCODE have generated a large body of genome-scale transcriptional data in multiple conditions (e.g., cell-types and disease states). Leveraging these data is especially important for network-based approaches to human disease, for instance to identify coherent transcriptional modules (subnetworks) that can inform functional disease mechanisms and pathological pathways. Yet, genome-scale network analysis across conditions is significantly hampered by the paucity of robust and computationally-efficient methods. Building on the Higher-Order Generalized Singular Value Decomposition, we introduce a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions. Our method can accommodate weighted (and unweighted) networks of any size and can similarly use co-expression or raw gene expression input data, without hinging upon the definition and stability of the correlation used to assess gene co-expression. In simulation studies, we demonstrated distinctive advantages of our method over existing methods, which was able to recover accurately both common and condition-specific network-modules without entailing ad-hoc input parameters as required by other approaches. We applied our method to genome-scale and multi-tissue transcriptomic datasets from rats (microarray-based) and humans (mRNA-sequencing-based) and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods. In humans we recapitulated the crosstalk between cell-cycle progression and cell-extracellular matrix interactions processes in ventricular zones during neocortex expansion and further, we uncovered pathways related to development of later cognitive functions in the cortical plate of the developing brain which were previously unappreciated. Analyses of seven rat tissues identified a multi-tissue subnetwork of co-expressed heat shock protein (Hsp) and cardiomyopathy genes (Bag3, Cryab, Kras, Emd, Plec), which was significantly replicated using separate failing heart and liver gene expression datasets in humans, thus revealing a conserved functional role for Hsp genes in cardiovascular disease.

Biological neural networks as model systems for designing future parallel processing computers

NASA Technical Reports Server (NTRS)

Ross, Muriel D.

1991-01-01

One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

Trade in and Valuation of Virtual Water Impacts in a City: A Case Study Of Flagstaff, Arizona

NASA Astrophysics Data System (ADS)

Rushforth, R.; Ruddell, B. L.

2013-12-01

An increasingly intense component of the global coupled natural and human system (CNH) is the economic trade of various types of resources and the outsourcing of resource impacts between geographically distant economic systems. The human economy's trade arrangements allow specific localities, especially cities, to exceed spatially local resource stock sustainability and footprint constraints, as evidenced in the urban metabolism literature. Each movement or trade of a resource along a network is associated with an embedded or 'virtual' exchange of indirect impacts on the inputs to the production process. The networked trade of embedded resources, therefore, is an essential human adaptation to resource limitations. Using the Embedded Resource Impact Accounting (ERA) framework, we examine the network of embedded water flows created through the trade of goods and services and economic development in Flagstaff, Arizona, and associate these flows with the creation of value in sectors of the economy

Investigation of automated task learning, decomposition and scheduling

NASA Technical Reports Server (NTRS)

Livingston, David L.; Serpen, Gursel; Masti, Chandrashekar L.

1990-01-01

The details and results of research conducted in the application of neural networks to task planning and decomposition are presented. Task planning and decomposition are operations that humans perform in a reasonably efficient manner. Without the use of good heuristics and usually much human interaction, automatic planners and decomposers generally do not perform well due to the intractable nature of the problems under consideration. The human-like performance of neural networks has shown promise for generating acceptable solutions to intractable problems such as planning and decomposition. This was the primary reasoning behind attempting the study. The basis for the work is the use of state machines to model tasks. State machine models provide a useful means for examining the structure of tasks since many formal techniques have been developed for their analysis and synthesis. It is the approach to integrate the strong algebraic foundations of state machines with the heretofore trial-and-error approach to neural network synthesis.

Adapting Concepts from Systems Biology to Develop Systems Exposure Event Networks for Exposure Science Research

EPA Science Inventory

Systems exposure science has emerged from the traditional environmental exposure assessment framework and incorporates new concepts that link sources of human exposure to internal dose and metabolic processes. Because many human environmental studies are designed for retrospectiv...

Scaling of the surface vasculature on the human placenta

NASA Astrophysics Data System (ADS)

Leonard, A. S.; Lee, J.; Schubert, D.; Croen, L. A.; Fallin, M. D.; Newschaffer, C. J.; Walker, C. K.; Salafia, C. M.; Morgan, S. P.; Vvedensky, D. D.

2017-10-01

The networks of veins and arteries on the chorionic plate of the human placenta are analyzed in terms of Voronoi cells derived from these networks. Two groups of placentas from the United States are studied: a population cohort with no prescreening, and a cohort from newborns with an elevated risk of developing autistic spectrum disorder. Scaled distributions of the Voronoi cell areas in the two cohorts collapse onto a single distribution, indicating common mechanisms for the formation of the complete vasculatures, but which have different levels of activity in the two cohorts.

Identification of a neuronal transcription factor network involved in medulloblastoma development

PubMed Central

2013-01-01

Background Medulloblastomas, the most frequent malignant brain tumours affecting children, comprise at least 4 distinct clinicogenetic subgroups. Aberrant sonic hedgehog (SHH) signalling is observed in approximately 25% of tumours and defines one subgroup. Although alterations in SHH pathway genes (e.g. PTCH1, SUFU) are observed in many of these tumours, high throughput genomic analyses have identified few other recurring mutations. Here, we have mutagenised the Ptch+/- murine tumour model using the Sleeping Beauty transposon system to identify additional genes and pathways involved in SHH subgroup medulloblastoma development. Results Mutagenesis significantly increased medulloblastoma frequency and identified 17 candidate cancer genes, including orthologs of genes somatically mutated (PTEN, CREBBP) or associated with poor outcome (PTEN, MYT1L) in the human disease. Strikingly, these candidate genes were enriched for transcription factors (p=2x10-5), the majority of which (6/7; Crebbp, Myt1L, Nfia, Nfib, Tead1 and Tgif2) were linked within a single regulatory network enriched for genes associated with a differentiated neuronal phenotype. Furthermore, activity of this network varied significantly between the human subgroups, was associated with metastatic disease, and predicted poor survival specifically within the SHH subgroup of tumours. Igf2, previously implicated in medulloblastoma, was the most differentially expressed gene in murine tumours with network perturbation, and network activity in both mouse and human tumours was characterised by enrichment for multiple gene-sets indicating increased cell proliferation, IGF signalling, MYC target upregulation, and decreased neuronal differentiation. Conclusions Collectively, our data support a model of medulloblastoma development in SB-mutagenised Ptch+/- mice which involves disruption of a novel transcription factor network leading to Igf2 upregulation, proliferation of GNPs, and tumour formation. Moreover, our results identify rational therapeutic targets for SHH subgroup tumours, alongside prognostic biomarkers for the identification of poor-risk SHH patients. PMID:24252690

Annual Research Review: Growth connectomics – the organization and reorganization of brain networks during normal and abnormal development

PubMed Central

Vértes, Petra E; Bullmore, Edward T

2015-01-01

Background We first give a brief introduction to graph theoretical analysis and its application to the study of brain network topology or connectomics. Within this framework, we review the existing empirical data on developmental changes in brain network organization across a range of experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans). Synthesis We discuss preliminary evidence and current hypotheses for how the emergence of network properties correlates with concomitant cognitive and behavioural changes associated with development. We highlight some of the technical and conceptual challenges to be addressed by future developments in this rapidly moving field. Given the parallels previously discovered between neural systems across species and over a range of spatial scales, we also review some recent advances in developmental network studies at the cellular scale. We highlight the opportunities presented by such studies and how they may complement neuroimaging in advancing our understanding of brain development. Finally, we note that many brain and mind disorders are thought to be neurodevelopmental in origin and that charting the trajectory of brain network changes associated with healthy development also sets the stage for understanding abnormal network development. Conclusions We therefore briefly review the clinical relevance of network metrics as potential diagnostic markers and some recent efforts in computational modelling of brain networks which might contribute to a more mechanistic understanding of neurodevelopmental disorders in future. PMID:25441756

Higher Education Staff Development: Directions for the 21st Century.

ERIC Educational Resources Information Center

Barnes, Jennifer; And Others

This collection of 13 papers offers an international perspective on future directions of staff development at colleges and universities, focusing on academic staff development, higher education teaching networks, and managerial and human resource development. Papers are: (1) "Higher Education Staff Development for the 21st Century: Directions…

Current Research in European Vocational Education and Human Resource Development. Proceedings of the Programme Presented by the Research Network on Vocational Education and Training (VETNET) at the European Conference of Educational Research (ECER) (4th, Lille, France, September 5-8, 2001).

ERIC Educational Resources Information Center

Manning, Sabine, Ed.; Dif, M'Hamed, Ed.

These proceedings are comprised of 23 presentations on research in European vocational education and human resource development. Papers include "Developing Information and Communication Technology Capability in Higher Education in the United Kingdom (UK)" (Nick Boreham); "Methodological Issues in the Study of Organizational…

Fractal analysis on human dynamics of library loans

NASA Astrophysics Data System (ADS)

Fan, Chao; Guo, Jin-Li; Zha, Yi-Long

2012-12-01

In this paper, the fractal characteristic of human behaviors is investigated from the perspective of time series constructed with the amount of library loans. The values of the Hurst exponent and length of non-periodic cycle calculated through rescaled range analysis indicate that the time series of human behaviors and their sub-series are fractal with self-similarity and long-range dependence. Then the time series are converted into complex networks by the visibility algorithm. The topological properties of the networks such as scale-free property and small-world effect imply that there is a close relationship among the numbers of repetitious behaviors performed by people during certain periods of time. Our work implies that there is intrinsic regularity in the human collective repetitious behaviors. The conclusions may be helpful to develop some new approaches to investigate the fractal feature and mechanism of human dynamics, and provide some references for the management and forecast of human collective behaviors.

Combatting Global Infectious Diseases: A Network Effect of Specimen Referral Systems.

PubMed

Fonjungo, Peter N; Alemnji, George A; Kebede, Yenew; Opio, Alex; Mwangi, Christina; Spira, Thomas J; Beard, R Suzanne; Nkengasong, John N

2017-02-13

The recent Ebola virus outbreak in West Africa clearly demonstrated the critical role of laboratory systems and networks in responding to epidemics. Because of the huge challenges in establishing functional laboratories at all tiers of health systems in developing countries, strengthening specimen referral networks is critical. In this review article, we propose a platform strategy for developing specimen referral networks based on 2 models: centralized and decentralized laboratory specimen referral networks. These models have been shown to be effective in patient management in programs in resource-limited settings. Both models lead to reduced turnaround time and retain flexibility for integrating different specimen types. In Haiti, decentralized specimen referral systems resulted in a 182% increase in patients enrolling in human immunodeficiency virus treatment programs within 6 months. In Uganda, cost savings of up to 62% were observed with a centralized model. A platform strategy will create a network effect that will benefit multiple disease programs.

Evolving the NASA Near Earth Network for the Next Generation of Human Space Flight

NASA Technical Reports Server (NTRS)

Roberts, Christopher J.; Carter, David L.; Hudiburg, John J.; Tye, Robert N.; Celeste, Peter B.

2014-01-01

The purpose of this paper is to present the planned development and evolution of the NASA Near Earth Network (NEN) launch communications services in support of the next generation of human space flight programs. Following the final space shuttle mission in 2011, the two NEN launch communications stations were decommissioned. Today, NASA is developing the next generation of human space flight systems focused on exploration missions beyond low-earth orbit, and supporting the emerging market for commercial crew and cargo human space flight services. The NEN is leading a major initiative to develop a modern high data rate launch communications ground architecture with support from the Kennedy Space Center Ground Systems Development and Operations Program and in partnership with the U.S. Air Force (USAF) Eastern Range. This initiative, the NEN Launch Communications Stations (LCS) development project, successfully completed its System Requirements Review in November 2013. This paper provides an overview of the LCS project and a summary of its progress. The LCS ground architecture, concept of operations, and driving requirements to support the new heavy-lift Space Launch System and Orion Multi-Purpose Crew Vehicle for Exploration Mission-1 are presented. Finally, potential future extensions to the ground architecture beyond EM-1 are discussed.

Foxp2 Regulates Gene Networks Implicated in Neurite Outgrowth in the Developing Brain

PubMed Central

Vernes, Sonja C.; Oliver, Peter L.; Spiteri, Elizabeth; Lockstone, Helen E.; Puliyadi, Rathi; Taylor, Jennifer M.; Ho, Joses; Mombereau, Cedric; Brewer, Ariel; Lowy, Ernesto; Nicod, Jérôme; Groszer, Matthias; Baban, Dilair; Sahgal, Natasha; Cazier, Jean-Baptiste; Ragoussis, Jiannis; Davies, Kay E.; Geschwind, Daniel H.; Fisher, Simon E.

2011-01-01

Forkhead-box protein P2 is a transcription factor that has been associated with intriguing aspects of cognitive function in humans, non-human mammals, and song-learning birds. Heterozygous mutations of the human FOXP2 gene cause a monogenic speech and language disorder. Reduced functional dosage of the mouse version (Foxp2) causes deficient cortico-striatal synaptic plasticity and impairs motor-skill learning. Moreover, the songbird orthologue appears critically important for vocal learning. Across diverse vertebrate species, this well-conserved transcription factor is highly expressed in the developing and adult central nervous system. Very little is known about the mechanisms regulated by Foxp2 during brain development. We used an integrated functional genomics strategy to robustly define Foxp2-dependent pathways, both direct and indirect targets, in the embryonic brain. Specifically, we performed genome-wide in vivo ChIP–chip screens for Foxp2-binding and thereby identified a set of 264 high-confidence neural targets under strict, empirically derived significance thresholds. The findings, coupled to expression profiling and in situ hybridization of brain tissue from wild-type and mutant mouse embryos, strongly highlighted gene networks linked to neurite development. We followed up our genomics data with functional experiments, showing that Foxp2 impacts on neurite outgrowth in primary neurons and in neuronal cell models. Our data indicate that Foxp2 modulates neuronal network formation, by directly and indirectly regulating mRNAs involved in the development and plasticity of neuronal connections. PMID:21765815

Foxp2 regulates gene networks implicated in neurite outgrowth in the developing brain.

PubMed

Vernes, Sonja C; Oliver, Peter L; Spiteri, Elizabeth; Lockstone, Helen E; Puliyadi, Rathi; Taylor, Jennifer M; Ho, Joses; Mombereau, Cedric; Brewer, Ariel; Lowy, Ernesto; Nicod, Jérôme; Groszer, Matthias; Baban, Dilair; Sahgal, Natasha; Cazier, Jean-Baptiste; Ragoussis, Jiannis; Davies, Kay E; Geschwind, Daniel H; Fisher, Simon E

2011-07-01

Forkhead-box protein P2 is a transcription factor that has been associated with intriguing aspects of cognitive function in humans, non-human mammals, and song-learning birds. Heterozygous mutations of the human FOXP2 gene cause a monogenic speech and language disorder. Reduced functional dosage of the mouse version (Foxp2) causes deficient cortico-striatal synaptic plasticity and impairs motor-skill learning. Moreover, the songbird orthologue appears critically important for vocal learning. Across diverse vertebrate species, this well-conserved transcription factor is highly expressed in the developing and adult central nervous system. Very little is known about the mechanisms regulated by Foxp2 during brain development. We used an integrated functional genomics strategy to robustly define Foxp2-dependent pathways, both direct and indirect targets, in the embryonic brain. Specifically, we performed genome-wide in vivo ChIP-chip screens for Foxp2-binding and thereby identified a set of 264 high-confidence neural targets under strict, empirically derived significance thresholds. The findings, coupled to expression profiling and in situ hybridization of brain tissue from wild-type and mutant mouse embryos, strongly highlighted gene networks linked to neurite development. We followed up our genomics data with functional experiments, showing that Foxp2 impacts on neurite outgrowth in primary neurons and in neuronal cell models. Our data indicate that Foxp2 modulates neuronal network formation, by directly and indirectly regulating mRNAs involved in the development and plasticity of neuronal connections.

Extended mind and after: socially extended mind and actor-network.

PubMed

Kono, Tetsuya

2014-03-01

The concept of extended mind has been impressively developed over the last 10 years by many philosophers and cognitive scientists. The extended mind thesis (EM) affirms that the mind is not simply ensconced inside the head, but extends to the whole system of brain-body-environment. Recently, some philosophers and psychologists try to adapt the idea of EM to the domain of social cognition research. Mind is socially extended (SEM). However, EM/SEM theory has problems to analyze the interactions among a subject and its surroundings with opposition, antagonism, or conflict; it also tends to think that the environment surrounding the subject is passive or static, and to neglect the power of non-human actants to direct and regulate the human subject. In these points, actor-network theory (ANT) proposed by Latour and Callon is more persuasive, while sharing some important ideas with EM/SEM theory. Actor-network is a hybrid community which is composed of a series of heterogeneous elements, animate and inanimate for a certain period of time. I shall conclude that EM/SEM could be best analyzed as a special case of actor-network. EM/SEM is a system which can be controlled by a human agent alone. In order to understand collective behavior, philosophy and psychology have to study the actor-network in which human individuals are situated.

Construction of multi-scale consistent brain networks: methods and applications.

PubMed

Ge, Bao; Tian, Yin; Hu, Xintao; Chen, Hanbo; Zhu, Dajiang; Zhang, Tuo; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.

Molecular and functional definition of the developing human striatum.

PubMed

Onorati, Marco; Castiglioni, Valentina; Biasci, Daniele; Cesana, Elisabetta; Menon, Ramesh; Vuono, Romina; Talpo, Francesca; Laguna Goya, Rocio; Lyons, Paul A; Bulfamante, Gaetano P; Muzio, Luca; Martino, Gianvito; Toselli, Mauro; Farina, Cinthia; Barker, Roger A; Biella, Gerardo; Cattaneo, Elena

2014-12-01

The complexity of the human brain derives from the intricate interplay of molecular instructions during development. Here we systematically investigated gene expression changes in the prenatal human striatum and cerebral cortex during development from post-conception weeks 2 to 20. We identified tissue-specific gene coexpression networks, differentially expressed genes and a minimal set of bimodal genes, including those encoding transcription factors, that distinguished striatal from neocortical identities. Unexpected differences from mouse striatal development were discovered. We monitored 36 determinants at the protein level, revealing regional domains of expression and their refinement, during striatal development. We electrophysiologically profiled human striatal neurons differentiated in vitro and determined their refined molecular and functional properties. These results provide a resource and opportunity to gain global understanding of how transcriptional and functional processes converge to specify human striatal and neocortical neurons during development.

Dynamical Integration of Language and Behavior in a Recurrent Neural Network for Human-Robot Interaction.

PubMed

Yamada, Tatsuro; Murata, Shingo; Arie, Hiroaki; Ogata, Tetsuya

2016-01-01

To work cooperatively with humans by using language, robots must not only acquire a mapping between language and their behavior but also autonomously utilize the mapping in appropriate contexts of interactive tasks online. To this end, we propose a novel learning method linking language to robot behavior by means of a recurrent neural network. In this method, the network learns from correct examples of the imposed task that are given not as explicitly separated sets of language and behavior but as sequential data constructed from the actual temporal flow of the task. By doing this, the internal dynamics of the network models both language-behavior relationships and the temporal patterns of interaction. Here, "internal dynamics" refers to the time development of the system defined on the fixed-dimensional space of the internal states of the context layer. Thus, in the execution phase, by constantly representing where in the interaction context it is as its current state, the network autonomously switches between recognition and generation phases without any explicit signs and utilizes the acquired mapping in appropriate contexts. To evaluate our method, we conducted an experiment in which a robot generates appropriate behavior responding to a human's linguistic instruction. After learning, the network actually formed the attractor structure representing both language-behavior relationships and the task's temporal pattern in its internal dynamics. In the dynamics, language-behavior mapping was achieved by the branching structure. Repetition of human's instruction and robot's behavioral response was represented as the cyclic structure, and besides, waiting to a subsequent instruction was represented as the fixed-point attractor. Thanks to this structure, the robot was able to interact online with a human concerning the given task by autonomously switching phases.

Predict human body indentation lying on a spring mattress using a neural network approach.

PubMed

Zhong, Shilu; Shen, Liming; Zhou, Lijuan; Guan, Zhongwei

2014-08-01

This article presents a method to predict and assess the interaction between a human body and a spring mattress. A three-layer artificial neural network model was developed to simulate and predict an indentation curve of human spine, characterized with the depth of lumbar lordosis and four inclination angles: cervicothoracic, thoracolumbar, lumbosacral and the back-hip (β). By comparing the spinal indentation curves described by the optimal evaluation parameters (depth of lumbar lordosis, cervicothoracic, thoracolumbar and lumbosacral), a better design of five-zone spring mattresses was obtained for individuals to have an effective support to the main part of the body. Using such approach, an operating process was further introduced, in which appropriate stiffness proportions were proposed to design mattress for the normal body types of Chinese young women. Finally, case studies were undertaken, which show that the method developed is feasible and practical. © IMechE 2014.

A Novel Human Body Area Network for Brain Diseases Analysis.

PubMed

Lin, Kai; Xu, Tianlang

2016-10-01

Development of wireless sensor and mobile communication technology provide an unprecedented opportunity for realizing smart and interactive healthcare systems. Designing such systems aims to remotely monitor the health and diagnose the diseases for users. In this paper, we design a novel human body area network for brain diseases analysis, which is named BABDA. Considering the brain is one of the most complex organs in the human body, the BABDA system provides four function modules to ensure the high quality of the analysis result, which includes initial data collection, data correction, data transmission and comprehensive data analysis. The performance evaluation conducted in a realistic environment with several criteria shows the availability and practicability of the BABDA system.

Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture

PubMed Central

Ferguson, Michael A.; Anderson, Jeffrey S.; Spreng, R. Nathan

2017-01-01

Human intelligence has been conceptualized as a complex system of dissociable cognitive processes, yet studies investigating the neural basis of intelligence have typically emphasized the contributions of discrete brain regions or, more recently, of specific networks of functionally connected regions. Here we take a broader, systems perspective in order to investigate whether intelligence is an emergent property of synchrony within the brain’s intrinsic network architecture. Using a large sample of resting-state fMRI and cognitive data (n = 830), we report that the synchrony of functional interactions within and across distributed brain networks reliably predicts fluid and flexible intellectual functioning. By adopting a whole-brain, systems-level approach, we were able to reliably predict individual differences in human intelligence by characterizing features of the brain’s intrinsic network architecture. These findings hold promise for the eventual development of neural markers to predict changes in intellectual function that are associated with neurodevelopment, normal aging, and brain disease.

A network engineering perspective on probing and perturbing cognition with neurofeedback.

PubMed

Bassett, Danielle S; Khambhati, Ankit N

2017-05-01

Network science and engineering provide a flexible and generalizable tool set to describe and manipulate complex systems characterized by heterogeneous interaction patterns among component parts. While classically applied to social systems, these tools have recently proven to be particularly useful in the study of the brain. In this review, we describe the nascent use of these tools to understand human cognition, and we discuss their utility in informing the meaningful and predictable perturbation of cognition in combination with the emerging capabilities of neurofeedback. To blend these disparate strands of research, we build on emerging conceptualizations of how the brain functions (as a complex network) and how we can develop and target interventions or modulations (as a form of network control). We close with an outline of current frontiers that bridge neurofeedback, connectomics, and network control theory to better understand human cognition. © 2017 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals Inc. on behalf of The New York Academy of Sciences.

Use of limited data to construct Bayesian networks for probabilistic risk assessment.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Groth, Katrina M.; Swiler, Laura Painton

2013-03-01

Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was tomore » establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.« less

Advanced Ring-Shaped Microelectrode Assay Combined with Small Rectangular Electrode for Quasi-In vivo Measurement of Cell-to-Cell Conductance in Cardiomyocyte Network

NASA Astrophysics Data System (ADS)

Nomura, Fumimasa; Kaneko, Tomoyuki; Hamada, Tomoyo; Hattori, Akihiro; Yasuda, Kenji

2013-06-01

To predict the risk of fatal arrhythmia induced by cardiotoxicity in the highly complex human heart system, we have developed a novel quasi-in vivo electrophysiological measurement assay, which combines a ring-shaped human cardiomyocyte network and a set of two electrodes that form a large single ring-shaped electrode for the direct measurement of irregular cell-to-cell conductance occurrence in a cardiomyocyte network, and a small rectangular microelectrode for forced pacing of cardiomyocyte beating and for acquiring the field potential waveforms of cardiomyocytes. The advantages of this assay are as follows. The electrophysiological signals of cardiomyocytes in the ring-shaped network are superimposed directly on a single loop-shaped electrode, in which the information of asynchronous behavior of cell-to-cell conductance are included, without requiring a set of huge numbers of microelectrode arrays, a set of fast data conversion circuits, or a complex analysis in a computer. Another advantage is that the small rectangular electrode can control the position and timing of forced beating in a ring-shaped human induced pluripotent stem cell (hiPS)-derived cardiomyocyte network and can also acquire the field potentials of cardiomyocytes. First, we constructed the human iPS-derived cardiomyocyte ring-shaped network on the set of two electrodes, and acquired the field potential signals of particular cardiomyocytes in the ring-shaped cardiomyocyte network during simultaneous acquisition of the superimposed signals of whole-cardiomyocyte networks representing cell-to-cell conduction. Using the small rectangular electrode, we have also evaluated the response of the cell network to electrical stimulation. The mean and SD of the minimum stimulation voltage required for pacing (VMin) at the small rectangular electrode was 166+/-74 mV, which is the same as the magnitude of amplitude for the pacing using the ring-shaped electrode (179+/-33 mV). The results showed that the addition of a small rectangular electrode into the ring-shaped electrode was effective for the simultaneous measurement of whole-cell-network signals and single-cell/small-cluster signals on a local site in the cell network, and for the pacing by electrical stimulation of cardiomyocyte networks.

Social Networks and Welfare in Future Animal Management

PubMed Central

Koene, Paul; Ipema, Bert

2014-01-01

Simple Summary Living in a stable social environment is important to animals. Animal species have developed social behaviors and rules of approach and avoidance of conspecifics in order to co-exist. Animal species are kept or domesticated without explicit regard for their inherent social behavior and rules. Examples of social structures are provided for four species kept and managed by humans. This information is important for the welfare management of these species. In the near future, automatic measurement of social structures will provide a tool for daily welfare management together with nearest neighbor information. Abstract It may become advantageous to keep human-managed animals in the social network groups to which they have adapted. Data concerning the social networks of farm animal species and their ancestors are scarce but essential to establishing the importance of a natural social network for farmed animal species. Social Network Analysis (SNA) facilitates the characterization of social networking at group, subgroup and individual levels. SNA is currently used for modeling the social behavior and management of wild animals and social welfare of zoo animals. It has been recognized for use with farm animals but has yet to be applied for management purposes. Currently, the main focus is on cattle, because in large groups (poultry), recording of individuals is expensive and the existence of social networks is uncertain due to on-farm restrictions. However, in many cases, a stable social network might be important to individual animal fitness, survival and welfare. For instance, when laying hens are not too densely housed, simple networks may be established. We describe here small social networks in horses, brown bears, laying hens and veal calves to illustrate the importance of measuring social networks among animals managed by humans. Emphasis is placed on the automatic measurement of identity, location, nearest neighbors and nearest neighbor distance for management purposes. It is concluded that social networks are important to the welfare of human-managed animal species and that welfare management based on automatic recordings will become available in the near future. PMID:26479886

Developmental Reorganization of the Core and Extended Face Networks Revealed by Global Functional Connectivity.

PubMed

Wang, Xu; Zhu, Qi; Song, Yiying; Liu, Jia

2017-08-28

Prior studies on development of functional specialization in human brain mainly focus on age-related increases in regional activation and connectivity among regions. However, a few recent studies on the face network demonstrate age-related decrease in face-specialized activation in the extended face network (EFN), in addition to increase in activation in the core face network (CFN). Here we used a voxel-based global brain connectivity approach to investigate whether development of the face network exhibited both increase and decrease in network connectivity. We found the voxel-wise resting-state functional connectivity (FC) within the CFN increased with age in bilateral posterior superior temporal sulcus, suggesting the integration of the CFN during development. Interestingly, the FC of the voxels in the EFN to the right fusiform face area and occipital face area decreased with age, suggesting that the CFN segregated from the EFN during development. Moreover, the age-related connectivity in the CFN was related to behavioral performance in face processing. Overall, our study demonstrated developmental reorganization of the face network achieved by both integration within the CFN and segregation of the CFN from the EFN, which may account for the simultaneous increases and decreases in neural activation during the development of the face network. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders.

PubMed

Fang, Jiansong; Gao, Li; Ma, Huili; Wu, Qihui; Wu, Tian; Wu, Jun; Wang, Qi; Cheng, Feixiong

2017-01-01

Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), c aenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders.

Quantitative and Systems Pharmacology 3. Network-Based Identification of New Targets for Natural Products Enables Potential Uses in Aging-Associated Disorders

PubMed Central

Fang, Jiansong; Gao, Li; Ma, Huili; Wu, Qihui; Wu, Tian; Wu, Jun; Wang, Qi; Cheng, Feixiong

2017-01-01

Aging that refers the accumulation of genetic and physiology changes in cells and tissues over a lifetime has been shown a high risk of developing various complex diseases, such as neurodegenerative disease, cardiovascular disease and cancer. Over the past several decades, natural products have been demonstrated as anti-aging interveners via extending lifespan and preventing aging-associated disorders. In this study, we developed an integrated systems pharmacology infrastructure to uncover new indications for aging-associated disorders by natural products. Specifically, we incorporated 411 high-quality aging-associated human genes or human-orthologous genes from mus musculus (MM), saccharomyces cerevisiae (SC), caenorhabditis elegans (CE), and drosophila melanogaster (DM). We constructed a global drug-target network of natural products by integrating both experimental and computationally predicted drug-target interactions (DTI). We further built the statistical network models for identification of new anti-aging indications of natural products through integration of the curated aging-associated genes and drug-target network of natural products. High accuracy was achieved on the network models. We showcased several network-predicted anti-aging indications of four typical natural products (caffeic acid, metformin, myricetin, and resveratrol) with new mechanism-of-actions. In summary, this study offers a powerful systems pharmacology infrastructure to identify natural products for treatment of aging-associated disorders. PMID:29093681

Cultured networks of excitatory projection neurons and inhibitory interneurons for studying human cortical neurotoxicity

PubMed Central

Xu, Jin-Chong; Fan, Jing; Wang, Xueqing; Eacker, Stephen M.; Kam, Tae-In; Chen, Li; Yin, Xiling; Zhu, Juehua; Chi, Zhikai; Jiang, Haisong; Chen, Rong; Dawson, Ted M.; Dawson, Valina L.

2017-01-01

Translating neuroprotective treatments from discovery in cell and animal models to the clinic has proven challenging. To reduce the gap between basic studies of neurotoxicity and neuroprotection and clinically relevant therapies, we developed a human cortical neuron culture system from human embryonic stem cells (ESCs) or inducible pluripotent stem cells (iPSCs) that generated both excitatory and inhibitory neuronal networks resembling the composition of the human cortex. This methodology used timed administration of retinoic acid (RA) to FOXG1 neural precursor cells leading to differentiation of neuronal populations representative of the six cortical layers with both excitatory and inhibitory neuronal networks that were functional and homeostatically stable. In human cortical neuron cultures, excitotoxicity or ischemia due to oxygen and glucose deprivation led to cell death that was dependent on N-methyl-D-aspartate (NMDA) receptors, nitric oxide (NO), and the poly (ADP-ribose) polymerase (PARP)-dependent cell death, a cell death pathway designated parthanatos to separate it from apoptosis, necroptosis and other forms of cell death. Neuronal cell death was attenuated by PARP inhibitors that are currently in clinical trials for cancer treatment. This culture system provides a new platform for the study of human cortical neurotoxicity and suggests that PARP inhibitors may be useful for ameliorating excitotoxic and ischemic cell death in human neurons. PMID:27053772

Use of Social Networking Sites by Academic Librarians in Six Selected States of Nigeria

ERIC Educational Resources Information Center

Tella, Adeyinka; Olarongbe, Shuaib Agboola; Akanbi-Ademolake, Hauwa Bolanle; Adisa, Mulikat Y.

2013-01-01

The attractiveness of social networking sites (SNSs) has extended to almost all professionals in numerous human organizations including the library. Librarians as a result of this development are now making use of these sites to connect to other libraries and librarians both within and outside their environment. However, it is observed that the…

Neurobehavioral Assessment from Fetus to Infant: The NICU Network Neurobehavioral Scale and the Fetal Neurobehavior Coding Scale

ERIC Educational Resources Information Center

Salisbury, Amy L.; Fallone, Melissa Duncan; Lester, Barry

2005-01-01

This review provides an overview and definition of the concept of neurobehavior in human development. Two neurobehavioral assessments used by the authors in current fetal and infant research are discussed: the NICU Network Neurobehavioral Assessment Scale and the Fetal Neurobehavior Coding System. This review will present how the two assessments…

Educating for an Inclusive World: Lessons Learned from a Globally Networked Human Rights and Disability Course for Social Work and Law Students

ERIC Educational Resources Information Center

Critelli, Filomena; Lewis, Laura; Méndez-López, Adalberto

2017-01-01

This article examines an innovative model of online international education regarding disability through a human rights perspective piloted through a collaboration between Universidad LaSalle, Mexico, and University at Buffalo, United States. The course is organized around a pressing global human rights and development issue. Its objective is to…

Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL.

PubMed

Della Gatta, Giusy; Palomero, Teresa; Perez-Garcia, Arianne; Ambesi-Impiombato, Alberto; Bansal, Mukesh; Carpenter, Zachary W; De Keersmaecker, Kim; Sole, Xavier; Xu, Luyao; Paietta, Elisabeth; Racevskis, Janis; Wiernik, Peter H; Rowe, Jacob M; Meijerink, Jules P; Califano, Andrea; Ferrando, Adolfo A

2012-02-26

The TLX1 and TLX3 transcription factor oncogenes have a key role in the pathogenesis of T cell acute lymphoblastic leukemia (T-ALL). Here we used reverse engineering of global transcriptional networks to decipher the oncogenic regulatory circuit controlled by TLX1 and TLX3. This systems biology analysis defined T cell leukemia homeobox 1 (TLX1) and TLX3 as master regulators of an oncogenic transcriptional circuit governing T-ALL. Notably, a network structure analysis of this hierarchical network identified RUNX1 as a key mediator of the T-ALL induced by TLX1 and TLX3 and predicted a tumor-suppressor role for RUNX1 in T cell transformation. Consistent with these results, we identified recurrent somatic loss-of-function mutations in RUNX1 in human T-ALL. Overall, these results place TLX1 and TLX3 at the top of an oncogenic transcriptional network controlling leukemia development, show the power of network analyses to identify key elements in the regulatory circuits governing human cancer and identify RUNX1 as a tumor-suppressor gene in T-ALL.

Structure, function, and control of the human musculoskeletal network

PubMed Central

Murphy, Andrew C.; Muldoon, Sarah F.; Baker, David; Lastowka, Adam; Bennett, Brittany; Yang, Muzhi

2018-01-01

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments. PMID:29346370

A network control theory approach to modeling and optimal control of zoonoses: case study of brucellosis transmission in sub-Saharan Africa.

PubMed

Roy, Sandip; McElwain, Terry F; Wan, Yan

2011-10-01

Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis) and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations), and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk) and points in the spread network (e.g., transhumance vs. sedentary herds). In addition, a preliminary identification of the network model for brucellosis is achieved using historical data, and the robustness of the obtained model is demonstrated. As a whole, our results indicate that network modeling can aid in designing control policies for zoonotic diseases.

A Network Control Theory Approach to Modeling and Optimal Control of Zoonoses: Case Study of Brucellosis Transmission in Sub-Saharan Africa

PubMed Central

Roy, Sandip; McElwain, Terry F.; Wan, Yan

2011-01-01

Background Developing control policies for zoonotic diseases is challenging, both because of the complex spread dynamics exhibited by these diseases, and because of the need for implementing complex multi-species surveillance and control efforts using limited resources. Mathematical models, and in particular network models, of disease spread are promising as tools for control-policy design, because they can provide comprehensive quantitative representations of disease transmission. Methodology/Principal Findings A layered dynamical network model for the transmission and control of zoonotic diseases is introduced as a tool for analyzing disease spread and designing cost-effective surveillance and control. The model development is achieved using brucellosis transmission among wildlife, cattle herds, and human sub-populations in an agricultural system as a case study. Precisely, a model that tracks infection counts in interacting animal herds of multiple species (e.g., cattle herds and groups of wildlife for brucellosis) and in human subpopulations is introduced. The model is then abstracted to a form that permits comprehensive targeted design of multiple control capabilities as well as model identification from data. Next, techniques are developed for such quantitative design of control policies (that are directed to both the animal and human populations), and for model identification from snapshot and time-course data, by drawing on recent results in the network control community. Conclusions/Significance The modeling approach is shown to provide quantitative insight into comprehensive control policies for zoonotic diseases, and in turn to permit policy design for mitigation of these diseases. For the brucellosis-transmission example in particular, numerous insights are obtained regarding the optimal distribution of resources among available control capabilities (e.g., vaccination, surveillance and culling, pasteurization of milk) and points in the spread network (e.g., transhumance vs. sedentary herds). In addition, a preliminary identification of the network model for brucellosis is achieved using historical data, and the robustness of the obtained model is demonstrated. As a whole, our results indicate that network modeling can aid in designing control policies for zoonotic diseases. PMID:22022621

Stage-specific differential gene expression profiling and functional network analysis during morphogenesis of diphyodont dentition in miniature pigs, Sus Scrofa

PubMed Central

2014-01-01

Background Our current knowledge of tooth development derives mainly from studies in mice, which have only one set of non-replaced teeth, compared with the diphyodont dentition in humans. The miniature pig is also diphyodont, making it a valuable alternative model for understanding human tooth development and replacement. However, little is known about gene expression and function during swine odontogenesis. The goal of this study is to undertake the survey of differential gene expression profiling and functional network analysis during morphogenesis of diphyodont dentition in miniature pigs. The identification of genes related to diphyodont development should lead to a better understanding of morphogenetic patterns and the mechanisms of diphyodont replacement in large animal models and humans. Results The temporal gene expression profiles during early diphyodont development in miniature pigs were detected with the Affymetrix Porcine GeneChip. The gene expression data were further evaluated by ANOVA as well as pathway and STC analyses. A total of 2,053 genes were detected with differential expression. Several signal pathways and 151 genes were then identified through the construction of pathway and signal networks. Conclusions The gene expression profiles indicated that spatio-temporal down-regulation patterns of gene expression were predominant; while, both dynamic activation and inhibition of pathways occurred during the morphogenesis of diphyodont dentition. Our study offers a mechanistic framework for understanding dynamic gene regulation of early diphyodont development and provides a molecular basis for studying teeth development, replacement, and regeneration in miniature pigs. PMID:24498892

Devices and circuits for nanoelectronic implementation of artificial neural networks

NASA Astrophysics Data System (ADS)

Turel, Ozgur

Biological neural networks perform complicated information processing tasks at speeds better than conventional computers based on conventional algorithms. This has inspired researchers to look into the way these networks function, and propose artificial networks that mimic their behavior. Unfortunately, most artificial neural networks, either software or hardware, do not provide either the speed or the complexity of a human brain. Nanoelectronics, with high density and low power dissipation that it provides, may be used in developing more efficient artificial neural networks. This work consists of two major contributions in this direction. First is the proposal of the CMOL concept, hybrid CMOS-molecular hardware [1-8]. CMOL may circumvent most of the problems in posed by molecular devices, such as low yield, vet provide high active device density, ˜1012/cm 2. The second contribution is CrossNets, artificial neural networks that are based on CMOL. We showed that CrossNets, with their fault tolerance, exceptional speed (˜ 4 to 6 orders of magnitude faster than biological neural networks) can perform any task any artificial neural network can perform. Moreover, there is a hope that if their integration scale is increased to that of human cerebral cortex (˜ 1010 neurons and ˜ 1014 synapses), they may be capable of performing more advanced tasks.

Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm

PubMed Central

Huang, Lei; Liao, Li; Wu, Cathy H.

2016-01-01

Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273

Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture.

PubMed

Ferrández-Pastor, Francisco Javier; García-Chamizo, Juan Manuel; Nieto-Hidalgo, Mario; Mora-Pascual, Jerónimo; Mora-Martínez, José

2016-07-22

The application of Information Technologies into Precision Agriculture methods has clear benefits. Precision Agriculture optimises production efficiency, increases quality, minimises environmental impact and reduces the use of resources (energy, water); however, there are different barriers that have delayed its wide development. Some of these main barriers are expensive equipment, the difficulty to operate and maintain and the standard for sensor networks are still under development. Nowadays, new technological development in embedded devices (hardware and communication protocols), the evolution of Internet technologies (Internet of Things) and ubiquitous computing (Ubiquitous Sensor Networks) allow developing less expensive systems, easier to control, install and maintain, using standard protocols with low-power consumption. This work develops and test a low-cost sensor/actuator network platform, based in Internet of Things, integrating machine-to-machine and human-machine-interface protocols. Edge computing uses this multi-protocol approach to develop control processes on Precision Agriculture scenarios. A greenhouse with hydroponic crop production was developed and tested using Ubiquitous Sensor Network monitoring and edge control on Internet of Things paradigm. The experimental results showed that the Internet technologies and Smart Object Communication Patterns can be combined to encourage development of Precision Agriculture. They demonstrated added benefits (cost, energy, smart developing, acceptance by agricultural specialists) when a project is launched.

Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture

PubMed Central

Ferrández-Pastor, Francisco Javier; García-Chamizo, Juan Manuel; Nieto-Hidalgo, Mario; Mora-Pascual, Jerónimo; Mora-Martínez, José

2016-01-01

The application of Information Technologies into Precision Agriculture methods has clear benefits. Precision Agriculture optimises production efficiency, increases quality, minimises environmental impact and reduces the use of resources (energy, water); however, there are different barriers that have delayed its wide development. Some of these main barriers are expensive equipment, the difficulty to operate and maintain and the standard for sensor networks are still under development. Nowadays, new technological development in embedded devices (hardware and communication protocols), the evolution of Internet technologies (Internet of Things) and ubiquitous computing (Ubiquitous Sensor Networks) allow developing less expensive systems, easier to control, install and maintain, using standard protocols with low-power consumption. This work develops and test a low-cost sensor/actuator network platform, based in Internet of Things, integrating machine-to-machine and human-machine-interface protocols. Edge computing uses this multi-protocol approach to develop control processes on Precision Agriculture scenarios. A greenhouse with hydroponic crop production was developed and tested using Ubiquitous Sensor Network monitoring and edge control on Internet of Things paradigm. The experimental results showed that the Internet technologies and Smart Object Communication Patterns can be combined to encourage development of Precision Agriculture. They demonstrated added benefits (cost, energy, smart developing, acceptance by agricultural specialists) when a project is launched. PMID:27455265

A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover (Charadrius melodus) using barrier island features

USGS Publications Warehouse

Gieder, Katherina D.; Karpanty, Sarah M.; Fraser, James D.; Catlin, Daniel H.; Gutierrez, Benjamin T.; Plant, Nathaniel G.; Turecek, Aaron M.; Thieler, E. Robert

2014-01-01

Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modelling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model’s dataset. We found that model predictions were more successful when the range of physical conditions included in model development was varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modelling impacts of sea-level rise- or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.

Physical parameters collection based on wireless senor network

NASA Astrophysics Data System (ADS)

Chen, Xin; Wu, Hong; Ji, Lei

2013-12-01

With the development of sensor technology, wireless senor network has been applied in the medical, military, entertainment field and our daily life. But the existing available wireless senor networks applied in human monitoring system still have some problems, such as big power consumption, low security and so on. To improve senor network applied in health monitoring system, the paper introduces a star wireless senor networks based on msp430 and DSP. We design a low-cost heart-rate monitor senor node. The communication between senor node and sink node is realized according to the newest protocol proposed by the IEEE 802.15.6 Task Group. This wireless senor network will be more energy-efficient and faster compared to traditional senor networks.

The Southeast Asian Influenza Clinical Research Network: development and challenges for a new multilateral research endeavor.

PubMed

Higgs, Elizabeth S; Hayden, Frederick G; Chotpitayasunondh, Tawee; Whitworth, Jimmy; Farrar, Jeremy

2008-04-01

The Southeast Asia Influenza Clinical Research Network (SEA ICRN) (www.seaclinicalresearch.org) is a recently developed multilateral, collaborative partnership that aims to advance scientific knowledge and management of human influenza through integrated clinical investigation. The partnership of hospitals and institutions in Indonesia, Thailand, United Kingdom, United States, and Viet Nam was established in late 2005 after agreement on the general principles and mission of the initiative and after securing initial financial support. The establishment of the SEA ICRN was both a response to the re-emergence of the highly pathogenic avian influenza A(H5N1) virus in Southeast Asia in late 2003 and an acknowledgment that clinical trials on emerging infectious diseases require prepared and coordinated research capacity. The objectives of the Network also include building sustainable research capacity in the region, compliance with international standards, and prompt dissemination of information and sharing of samples. The scope of research includes diagnosis, pathogenesis, treatment and prevention of human influenza due to seasonal or novel viruses. The Network has overcome numerous logistical and scientific challenges but has now successfully initiated several clinical trials. The establishment of a clinical research network is a vital part of preparedness and an important element during an initial response phase to a pandemic.

Developing AJN Network: phase two. An information resource for nurses.

PubMed Central

Rizzolo, M. A.; DuBois, K.

1995-01-01

In September of 1993 the American Journal of Nursing Company was awarded a three-year Special Projects Grant from the Division of Nursing, Department of Health and Human Services to develop a national information service that would provide a variety of formal and informal continuing education services to nurses in medically underserved communities. AJN Network went "live" in March 1994 and our progress in our first year of operation was presented at SCAMC in 1994. During the first year of operation, AJN Network was available through a dial in service. In September 1994 we became an Internet node. This presentation will detail our progress in Year 2 of the grant period, describing expansion of user base and content, new content offerings, initial stages of WEB development and plans for future development. PMID:8563246

Human-Systems Integration (HSI) and the Network Integration Evaluations (NIEs), Part 3: Mitigating Cognitive Load in Network-Enabled Mission Command

DTIC Science & Technology

2016-06-01

ARL-TR-7698 ● JUNE 2016 US Army Research Laboratory Human -Systems Integration (HSI) and the Network Integration Evaluations...ARL-TR-7698 ● JUNE 2016 US Army Research Laboratory Human -Systems Integration (HSI) and the Network Integration Evaluations (NIEs), Part 3...Mitigating Cognitive Load in Network-Enabled Mission Command by John K Hawley Human Research and Engineering Directorate, ARL Michael W

RICH Economic Games for Networked Relationships and Communities: Development and Preliminary Validation in Yasawa, Fiji

ERIC Educational Resources Information Center

Gervais, Matthew M.

2017-01-01

Experimental economic games reveal significant population variation in human social behavior. However, most protocols involve anonymous recipients, limiting their validity to fleeting interactions. Understanding human relationship dynamics will require methods with the virtues of economic games that also tap recipient identity-conditioned…

Systems and Algorithms for Automated Collaborative Observation Using Networked Robotic Cameras

ERIC Educational Resources Information Center

Xu, Yiliang

2011-01-01

The development of telerobotic systems has evolved from Single Operator Single Robot (SOSR) systems to Multiple Operator Multiple Robot (MOMR) systems. The relationship between human operators and robots follows the master-slave control architecture and the requests for controlling robot actuation are completely generated by human operators. …

Human tracking over camera networks: a review

NASA Astrophysics Data System (ADS)

Hou, Li; Wan, Wanggen; Hwang, Jenq-Neng; Muhammad, Rizwan; Yang, Mingyang; Han, Kang

2017-12-01

In recent years, automated human tracking over camera networks is getting essential for video surveillance. The tasks of tracking human over camera networks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, ranging from security surveillance to retail and health care. This review paper surveys the most widely used techniques and recent advances for human tracking over camera networks. Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across non-overlapping cameras. The core techniques of human tracking within a camera are discussed based on two aspects, i.e., generative trackers and discriminative trackers. The core techniques of human tracking across non-overlapping cameras are then discussed based on the aspects of human re-identification, camera-link model-based tracking and graph model-based tracking. Our survey aims to address existing problems, challenges, and future research directions based on the analyses of the current progress made toward human tracking techniques over camera networks.

Theorising big IT programmes in healthcare: strong structuration theory meets actor-network theory.

PubMed

Greenhalgh, Trisha; Stones, Rob

2010-05-01

The UK National Health Service is grappling with various large and controversial IT programmes. We sought to develop a sharper theoretical perspective on the question "What happens - at macro-, meso- and micro-level - when government tries to modernise a health service with the help of big IT?" Using examples from data fragments at the micro-level of clinical work, we considered how structuration theory and actor-network theory (ANT) might be combined to inform empirical investigation. Giddens (1984) argued that social structures and human agency are recursively linked and co-evolve. ANT studies the relationships that link people and technologies in dynamic networks. It considers how discourses become inscribed in data structures and decision models of software, making certain network relations irreversible. Stones' (2005) strong structuration theory (SST) is a refinement of Giddens' work, systematically concerned with empirical research. It views human agents as linked in dynamic networks of position-practices. A quadripartite approcach considers [a] external social structures (conditions for action); [b] internal social structures (agents' capabilities and what they 'know' about the social world); [c] active agency and actions and [d] outcomes as they feed back on the position-practice network. In contrast to early structuration theory and ANT, SST insists on disciplined conceptual methodology and linking this with empirical evidence. In this paper, we adapt SST for the study of technology programmes, integrating elements from material interactionism and ANT. We argue, for example, that the position-practice network can be a socio-technical one in which technologies in conjunction with humans can be studied as 'actants'. Human agents, with their complex socio-cultural frames, are required to instantiate technology in social practices. Structurally relevant properties inscribed and embedded in technological artefacts constrain and enable human agency. The fortunes of healthcare IT programmes might be studied in terms of the interplay between these factors. Copyright 2010 Elsevier Ltd. All rights reserved.

Coordinated Gene Expression of Neuroinflammatory and Cell Signaling Markers in Dorsolateral Prefrontal Cortex during Human Brain Development and Aging

PubMed Central

Primiani, Christopher T.; Ryan, Veronica H.; Rao, Jagadeesh S.; Cam, Margaret C.; Ahn, Kwangmi; Modi, Hiren R.; Rapoport, Stanley I.

2014-01-01

Background Age changes in expression of inflammatory, synaptic, and neurotrophic genes are not well characterized during human brain development and senescence. Knowing these changes may elucidate structural, metabolic, and functional brain processes over the lifespan, as well vulnerability to neurodevelopmental or neurodegenerative diseases. Hypothesis Expression levels of inflammatory, synaptic, and neurotrophic genes in the human brain are coordinated over the lifespan and underlie changes in phenotypic networks or cascades. Methods We used a large-scale microarray dataset from human prefrontal cortex, BrainCloud, to quantify age changes over the lifespan, divided into Development (0 to 21 years, 87 brains) and Aging (22 to 78 years, 144 brains) intervals, in transcription levels of 39 genes. Results Gene expression levels followed different trajectories over the lifespan. Many changes were intercorrelated within three similar groups or clusters of genes during both Development and Aging, despite different roles of the gene products in the two intervals. During Development, changes were related to reported neuronal loss, dendritic growth and pruning, and microglial events; TLR4, IL1R1, NFKB1, MOBP, PLA2G4A, and PTGS2 expression increased in the first years of life, while expression of synaptic genes GAP43 and DBN1 decreased, before reaching plateaus. During Aging, expression was upregulated for potentially pro-inflammatory genes such as NFKB1, TRAF6, TLR4, IL1R1, TSPO, and GFAP, but downregulated for neurotrophic and synaptic integrity genes such as BDNF, NGF, PDGFA, SYN, and DBN1. Conclusions Coordinated changes in gene transcription cascades underlie changes in synaptic, neurotrophic, and inflammatory phenotypic networks during brain Development and Aging. Early postnatal expression changes relate to neuronal, glial, and myelin growth and synaptic pruning events, while late Aging is associated with pro-inflammatory and synaptic loss changes. Thus, comparable transcriptional regulatory networks that operate throughout the lifespan underlie different phenotypic processes during Aging compared to Development. PMID:25329999

Coordinated gene expression of neuroinflammatory and cell signaling markers in dorsolateral prefrontal cortex during human brain development and aging.

PubMed

Primiani, Christopher T; Ryan, Veronica H; Rao, Jagadeesh S; Cam, Margaret C; Ahn, Kwangmi; Modi, Hiren R; Rapoport, Stanley I

2014-01-01

Age changes in expression of inflammatory, synaptic, and neurotrophic genes are not well characterized during human brain development and senescence. Knowing these changes may elucidate structural, metabolic, and functional brain processes over the lifespan, as well vulnerability to neurodevelopmental or neurodegenerative diseases. Expression levels of inflammatory, synaptic, and neurotrophic genes in the human brain are coordinated over the lifespan and underlie changes in phenotypic networks or cascades. We used a large-scale microarray dataset from human prefrontal cortex, BrainCloud, to quantify age changes over the lifespan, divided into Development (0 to 21 years, 87 brains) and Aging (22 to 78 years, 144 brains) intervals, in transcription levels of 39 genes. Gene expression levels followed different trajectories over the lifespan. Many changes were intercorrelated within three similar groups or clusters of genes during both Development and Aging, despite different roles of the gene products in the two intervals. During Development, changes were related to reported neuronal loss, dendritic growth and pruning, and microglial events; TLR4, IL1R1, NFKB1, MOBP, PLA2G4A, and PTGS2 expression increased in the first years of life, while expression of synaptic genes GAP43 and DBN1 decreased, before reaching plateaus. During Aging, expression was upregulated for potentially pro-inflammatory genes such as NFKB1, TRAF6, TLR4, IL1R1, TSPO, and GFAP, but downregulated for neurotrophic and synaptic integrity genes such as BDNF, NGF, PDGFA, SYN, and DBN1. Coordinated changes in gene transcription cascades underlie changes in synaptic, neurotrophic, and inflammatory phenotypic networks during brain Development and Aging. Early postnatal expression changes relate to neuronal, glial, and myelin growth and synaptic pruning events, while late Aging is associated with pro-inflammatory and synaptic loss changes. Thus, comparable transcriptional regulatory networks that operate throughout the lifespan underlie different phenotypic processes during Aging compared to Development.

European training network on full-parallax imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Martínez-Corral, Manuel; Saavedra, Genaro

2017-05-01

Current displays are far from truly recreating visual reality. This requires a full-parallax display that can reproduce radiance field emanated from the real scenes. The develop-ment of such technology will require a new generation of researchers trained both in the physics, and in the biology of human vision. The European Training Network on Full-Parallax Imaging (ETN-FPI) aims at developing this new generation. Under H2020 funding ETN-FPI brings together 8 beneficiaries and 8 partner organizations from five EU countries with the aim of training 15 talented pre-doctoral students to become future research leaders in this area. In this contribution we will explain the main objectives of the network, and specifically the advances obtained at the University of Valencia.

ISS Mini AERCam Radio Frequency (RF) Coverage Analysis Using iCAT Development Tool

NASA Technical Reports Server (NTRS)

Bolen, Steve; Vazquez, Luis; Sham, Catherine; Fredrickson, Steven; Fink, Patrick; Cox, Jan; Phan, Chau; Panneton, Robert

2003-01-01

The long-term goals of the National Aeronautics and Space Administration's (NASA's) Human Exploration and Development of Space (HEDS) enterprise may require the development of autonomous free-flier (FF) robotic devices to operate within the vicinity of low-Earth orbiting spacecraft to supplement human extravehicular activities (EVAs) in space. Future missions could require external visual inspection of the spacecraft that would be difficult, or dangerous, for humans to perform. Under some circumstance, it may be necessary to employ an un-tethered communications link between the FF and the users. The interactive coverage analysis tool (ICAT) is a software tool that has been developed to perform critical analysis of the communications link performance for a FF operating in the vicinity of the International Space Station (ISS) external environment. The tool allows users to interactively change multiple parameters of the communications link parameters to efficiently perform systems engineering trades on network performance. These trades can be directly translated into design and requirements specifications. This tool significantly reduces the development time in determining a communications network topology by allowing multiple parameters to be changed, and the results of link coverage to be statistically characterized and plotted interactively.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Heng; Ye, Hao; Ng, Hui Wen

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. Furthermore, this algorithmmore » 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.« less

Development of an internet based system for modeling biotin metabolism using Bayesian networks.

PubMed

Zhou, Jinglei; Wang, Dong; Schlegel, Vicki; Zempleni, Janos

2011-11-01

Biotin is an essential water-soluble vitamin crucial for maintaining normal body functions. The importance of biotin for human health has been under-appreciated but there is plenty of opportunity for future research with great importance for human health. Currently, carrying out predictions of biotin metabolism involves tedious manual manipulations. In this paper, we report the development of BiotinNet, an internet based program that uses Bayesian networks to integrate published data on various aspects of biotin metabolism. Users can provide a combination of values on the levels of biotin related metabolites to obtain the predictions on other metabolites that are not specified. As an inherent feature of Bayesian networks, the uncertainty of the prediction is also quantified and reported to the user. This program enables convenient in silico experiments regarding biotin metabolism, which can help researchers design future experiments while new data can be continuously incorporated. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

PubMed Central

Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

2016-01-01

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

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

DOE PAGES

Luo, Heng; Ye, Hao; Ng, Hui Wen; ...

2016-08-25

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. Furthermore, this algorithmmore » 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.« less

Hydropower in the Southeast: Balancing Lakeview and Production Optimization

NASA Astrophysics Data System (ADS)

Engstrom, J.

2017-12-01

Hydropower is the most important source of renewable electricity in Southeastern U.S. However, the region is repeatedly struck by droughts, and there are many conflicting interests in the limited water resource. This study takes a historical perspective and investigates how hydropower production patterns have changed over time, considering both natural drivers and human dimensions. Hydropower production is strongly tied to the natural variability of large-scale atmospheric drivers (teleconnections) as they affect the water availability in the whole river system and partly also the market demand. To balance the water resource between different interests is a complex task, and the conflicting interests vary by basin, sometimes over a relatively small geographic area. Here road networks adjacent to the hydropower reservoirs are used as an indicator of human development and recreational activities. Through a network analysis of the historical development of road networks surrounding the reservoir, the local and regional conflicting interests are identified and the influence on renewable electricity production quantified.

Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

PubMed

Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

2015-01-01

Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

An automatic method to generate domain-specific investigator networks using PubMed abstracts.

PubMed

Yu, Wei; Yesupriya, Ajay; Wulf, Anja; Qu, Junfeng; Gwinn, Marta; Khoury, Muin J

2007-06-20

Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts. We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit) as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8%) and from 94.2% of HuGE PubMed records (accuracy 87.0). We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit), indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70-90% of the investigators/collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network. We successfully created a web-based prototype capable of creating domain-specific investigator networks based on an application that accurately generates detailed investigator profiles from PubMed abstracts combined with robust standard vocabularies. This approach could be used for other biomedical fields to efficiently establish domain-specific investigator networks.

An automatic method to generate domain-specific investigator networks using PubMed abstracts

PubMed Central

Yu, Wei; Yesupriya, Ajay; Wulf, Anja; Qu, Junfeng; Gwinn, Marta; Khoury, Muin J

2007-01-01

Background Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts. Results We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit) as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8%) and from 94.2% of HuGE PubMed records (accuracy 87.0). We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit), indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70–90% of the investigators/collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network. Conclusion We successfully created a web-based prototype capable of creating domain-specific investigator networks based on an application that accurately generates detailed investigator profiles from PubMed abstracts combined with robust standard vocabularies. This approach could be used for other biomedical fields to efficiently establish domain-specific investigator networks. PMID:17584920

Inborn errors of metabolism and the human interactome: a systems medicine approach.

PubMed

Woidy, Mathias; Muntau, Ania C; Gersting, Søren W

2018-02-05

The group of inborn errors of metabolism (IEM) displays a marked heterogeneity and IEM can affect virtually all functions and organs of the human organism; however, IEM share that their associated proteins function in metabolism. Most proteins carry out cellular functions by interacting with other proteins, and thus are organized in biological networks. Therefore, diseases are rarely the consequence of single gene mutations but of the perturbations caused in the related cellular network. Systematic approaches that integrate multi-omics and database information into biological networks have successfully expanded our knowledge of complex disorders but network-based strategies have been rarely applied to study IEM. We analyzed IEM on a proteome scale and found that IEM-associated proteins are organized as a network of linked modules within the human interactome of protein interactions, the IEM interactome. Certain IEM disease groups formed self-contained disease modules, which were highly interlinked. On the other hand, we observed disease modules consisting of proteins from many different disease groups in the IEM interactome. Moreover, we explored the overlap between IEM and non-IEM disease genes and applied network medicine approaches to investigate shared biological pathways, clinical signs and symptoms, and links to drug targets. The provided resources may help to elucidate the molecular mechanisms underlying new IEM, to uncover the significance of disease-associated mutations, to identify new biomarkers, and to develop novel therapeutic strategies.

Single-shot T2 mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network.

PubMed

Cai, Congbo; Wang, Chao; Zeng, Yiqing; Cai, Shuhui; Liang, Dong; Wu, Yawen; Chen, Zhong; Ding, Xinghao; Zhong, Jianhui

2018-04-24

An end-to-end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T 2 mapping from single-shot overlapping-echo detachment (OLED) planar imaging. The training dataset was obtained from simulations that were carried out on SPROM (Simulation with PRoduct Operator Matrix) software developed by our group. The relationship between the original OLED image containing two echo signals and the corresponding T 2 mapping was learned by ResNet training. After the ResNet was trained, it was applied to reconstruct the T 2 mapping from simulation and in vivo human brain data. Although the ResNet was trained entirely on simulated data, the trained network was generalized well to real human brain data. The results from simulation and in vivo human brain experiments show that the proposed method significantly outperforms the echo-detachment-based method. Reliable T 2 mapping with higher accuracy is achieved within 30 ms after the network has been trained, while the echo-detachment-based OLED reconstruction method took approximately 2 min. The proposed method will facilitate real-time dynamic and quantitative MR imaging via OLED sequence, and deep convolutional neural network has the potential to reconstruct maps from complex MRI sequences efficiently. © 2018 International Society for Magnetic Resonance in Medicine.

Mathematical Modeling and Evaluation of Human Motions in Physical Therapy Using Mixture Density Neural Networks

PubMed Central

Vakanski, A; Ferguson, JM; Lee, S

2016-01-01

Objective The objective of the proposed research is to develop a methodology for modeling and evaluation of human motions, which will potentially benefit patients undertaking a physical rehabilitation therapy (e.g., following a stroke or due to other medical conditions). The ultimate aim is to allow patients to perform home-based rehabilitation exercises using a sensory system for capturing the motions, where an algorithm will retrieve the trajectories of a patient’s exercises, will perform data analysis by comparing the performed motions to a reference model of prescribed motions, and will send the analysis results to the patient’s physician with recommendations for improvement. Methods The modeling approach employs an artificial neural network, consisting of layers of recurrent neuron units and layers of neuron units for estimating a mixture density function over the spatio-temporal dependencies within the human motion sequences. Input data are sequences of motions related to a prescribed exercise by a physiotherapist to a patient, and recorded with a motion capture system. An autoencoder subnet is employed for reducing the dimensionality of captured sequences of human motions, complemented with a mixture density subnet for probabilistic modeling of the motion data using a mixture of Gaussian distributions. Results The proposed neural network architecture produced a model for sets of human motions represented with a mixture of Gaussian density functions. The mean log-likelihood of observed sequences was employed as a performance metric in evaluating the consistency of a subject’s performance relative to the reference dataset of motions. A publically available dataset of human motions captured with Microsoft Kinect was used for validation of the proposed method. Conclusion The article presents a novel approach for modeling and evaluation of human motions with a potential application in home-based physical therapy and rehabilitation. The described approach employs the recent progress in the field of machine learning and neural networks in developing a parametric model of human motions, by exploiting the representational power of these algorithms to encode nonlinear input-output dependencies over long temporal horizons. PMID:28111643

Mathematical Modeling and Evaluation of Human Motions in Physical Therapy Using Mixture Density Neural Networks.

PubMed

Vakanski, A; Ferguson, J M; Lee, S

2016-12-01

The objective of the proposed research is to develop a methodology for modeling and evaluation of human motions, which will potentially benefit patients undertaking a physical rehabilitation therapy (e.g., following a stroke or due to other medical conditions). The ultimate aim is to allow patients to perform home-based rehabilitation exercises using a sensory system for capturing the motions, where an algorithm will retrieve the trajectories of a patient's exercises, will perform data analysis by comparing the performed motions to a reference model of prescribed motions, and will send the analysis results to the patient's physician with recommendations for improvement. The modeling approach employs an artificial neural network, consisting of layers of recurrent neuron units and layers of neuron units for estimating a mixture density function over the spatio-temporal dependencies within the human motion sequences. Input data are sequences of motions related to a prescribed exercise by a physiotherapist to a patient, and recorded with a motion capture system. An autoencoder subnet is employed for reducing the dimensionality of captured sequences of human motions, complemented with a mixture density subnet for probabilistic modeling of the motion data using a mixture of Gaussian distributions. The proposed neural network architecture produced a model for sets of human motions represented with a mixture of Gaussian density functions. The mean log-likelihood of observed sequences was employed as a performance metric in evaluating the consistency of a subject's performance relative to the reference dataset of motions. A publically available dataset of human motions captured with Microsoft Kinect was used for validation of the proposed method. The article presents a novel approach for modeling and evaluation of human motions with a potential application in home-based physical therapy and rehabilitation. The described approach employs the recent progress in the field of machine learning and neural networks in developing a parametric model of human motions, by exploiting the representational power of these algorithms to encode nonlinear input-output dependencies over long temporal horizons.

Using Smartphones to Detect Earthquakes

NASA Astrophysics Data System (ADS)

Kong, Q.; Allen, R. M.

2012-12-01

We are using the accelerometers in smartphones to record earthquakes. In the future, these smartphones may work as a supplement network to the current traditional network for scientific research and real-time applications. Given the potential number of smartphones, and small separation of sensors, this new type of seismic dataset has significant potential provides that the signal can be separated from the noise. We developed an application for android phones to record the acceleration in real time. These records can be saved on the local phone or transmitted back to a server in real time. The accelerometers in the phones were evaluated by comparing performance with a high quality accelerometer while located on controlled shake tables for a variety of tests. The results show that the accelerometer in the smartphone can reproduce the characteristic of the shaking very well, even the phone left freely on the shake table. The nature of these datasets is also quite different from traditional networks due to the fact that smartphones are moving around with their owners. Therefore, we must distinguish earthquake signals from other daily use. In addition to the shake table tests that accumulated earthquake records, we also recorded different human activities such as running, walking, driving etc. An artificial neural network based approach was developed to distinguish these different records. It shows a 99.7% successful rate of distinguishing earthquakes from the other typical human activities in our database. We are now at the stage ready to develop the basic infrastructure for a smartphone seismic network.

Age-associated changes in rich-club organisation in autistic and neurotypical human brains

PubMed Central

Watanabe, Takamitsu; Rees, Geraint

2015-01-01

Macroscopic structural networks in the human brain have a rich-club architecture comprising both highly inter-connected central regions and sparsely connected peripheral regions. Recent studies show that disruption of this functionally efficient organisation is associated with several psychiatric disorders. However, despite increasing attention to this network property, whether age-associated changes in rich-club organisation occur during human adolescence remains unclear. Here, analysing a publicly shared diffusion tensor imaging dataset, we found that, during adolescence, brains of typically developing (TD) individuals showed increases in rich-club organisation and inferred network functionality, whereas individuals with autism spectrum disorders (ASD) did not. These differences between TD and ASD groups were statistically significant for both structural and functional properties. Moreover, this typical age-related changes in rich-club organisation were characterised by progressive involvement of the right anterior insula. In contrast, in ASD individuals, did not show typical increases in grey matter volume, and this relative anatomical immaturity was correlated with the severity of ASD social symptoms. These results provide evidence that rich-club architecture is one of the bases of functionally efficient brain networks underpinning complex cognitive functions in adult human brains. Furthermore, our findings suggest that immature rich-club organisation might be associated with some neurodevelopmental disorders. PMID:26537477

A Model of Mental State Transition Network

NASA Astrophysics Data System (ADS)

Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo

Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.

Emerging hierarchies in dynamically adapting webs

NASA Astrophysics Data System (ADS)

Katifori, Eleni; Graewer, Johannes; Magnasco, Marcelo; Modes, Carl

Transport networks play a key role across four realms of eukaryotic life: slime molds, fungi, plants, and animals. In addition to the developmental algorithms that build them, many also employ adaptive strategies to respond to stimuli, damage, and other environmental changes. We model these adapting network architectures using a generic dynamical system on weighted graphs and find in simulation that these networks ultimately develop a hierarchical organization of the final weighted architecture accompanied by the formation of a system-spanning backbone. We quantify the hierarchical organization of the networks by developing an algorithm that decomposes the architecture to multiple scales and analyzes how the organization in each scale relates to that of the scale above and below it. The methodologies developed in this work are applicable to a wide range of systems including the slime mold physarum polycephalum, human microvasculature, and force chains in granular media.

Heterogeneous Wireless Mesh Network Technology Evaluation for Space Proximity and Surface Applications

NASA Technical Reports Server (NTRS)

DeCristofaro, Michael A.; Lansdowne, Chatwin A.; Schlesinger, Adam M.

2014-01-01

NASA has identified standardized wireless mesh networking as a key technology for future human and robotic space exploration. Wireless mesh networks enable rapid deployment, provide coverage in undeveloped regions. Mesh networks are also self-healing, resilient, and extensible, qualities not found in traditional infrastructure-based networks. Mesh networks can offer lower size, weight, and power (SWaP) than overlapped infrastructure-perapplication. To better understand the maturity, characteristics and capability of the technology, we developed an 802.11 mesh network consisting of a combination of heterogeneous commercial off-the-shelf devices and opensource firmware and software packages. Various streaming applications were operated over the mesh network, including voice and video, and performance measurements were made under different operating scenarios. During the testing several issues with the currently implemented mesh network technology were identified and outlined for future work.

Scaling up: human genetics as a Cold War network.

PubMed

Lindee, Susan

2014-09-01

In this commentary I explore how the papers here illuminate the processes of collection that have been so central to the history of human genetics since 1945. The development of human population genetics in the Cold War period produced databases and biobanks that have endured into the present, and that continue to be used and debated. In the decades after the bomb, scientists collected and transferred human biological materials and information from populations of interest, and as they moved these biological resources or biosocial resources acquired new meanings and uses. The papers here collate these practices and map their desires and ironies. They explore how a large international network of geneticists, biological anthropologists, virologists and other physicians and scientists interacted with local informants, research subjects and public officials. They also track the networks and standards that mobilized the transfer of information, genealogies, tissue and blood samples. As Joanna Radin suggests here, the massive collections of human biological materials and data were often understood to be resources for an "as-yet-unknown" future. The stories told here contain elements of surveillance, extraction, salvage and eschatology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Humane Education in Brazil: Organisation, Challenges and Opportunities.

PubMed

Bachinski, Róber; Tréz, Thales; Alves, Gutemberg G; de C M Garcia, Rita; Oliveira, Simone T; da S Alonso, Luciano; Heck, Júlio X; Dias, Claudia M C; Costa Neto, João M; Rocha, Alexandro A; Ruiz, Valeska R R; Paixão, Rita L

2015-11-01

Humane education and the debate on alternatives to harmful animal use for training is a relatively recent issue in Brazil. While animal use in secondary education has been illegal since the late 1970s, animal use in higher science education is widespread. However, alternatives to animal experiments in research and testing have recently received attention from the Government, especially after the first legislation on animal experiments was passed, in 2008. This article proposes that higher science education should be based on a critical and humane approach. It outlines the recent establishment of the Brazilian Network for Humane Education (RedEH), as a result of the project, Mapping Animal Use for Undergraduate Education in Brazil, which was recognised by the 2014 Lush Prize. The network aims to create a platform to promote change in science education in Brazil, starting by quantitatively and qualitatively understanding animal use, developing new approaches adapted to the current needs in Brazil and Latin America, and communicating these initiatives nationally. This paper explores the trajectory of alternatives and replacement methods to harmful animal use in training and education, as well as the status of humane education in Brazil, from the point of view of educators and researchers engaged with the network.

Graph theoretical modeling of baby brain networks.

PubMed

Zhao, Tengda; Xu, Yuehua; He, Yong

2018-06-12

The human brain undergoes explosive growth during the prenatal period and the first few postnatal years, establishing an early infrastructure for the later development of behaviors and cognitions. Revealing the developmental rules during the early phrase is essential in understanding the emergence of brain function and the origin of developmental disorders. The graph-theoretical network modeling in combination with multiple neuroimaging probes provides an important research framework to explore early development of the topological wiring and organizational paradigms of the brain. Here, we reviewed studies which employed neuroimaging and graph-theoretical modeling to investigate brain network development from approximately 20 gestational weeks to 2 years of age. Specifically, the structural and functional brain networks have evolved to highly efficient topological architectures in the early stage; where the structural network remains ahead and paves the way for the development of functional network. The brain network develops in a heterogeneous order, from primary to higher-order systems and from a tendency of network segregation to network integration in the prenatal and postnatal periods. The early brain network topologies show abilities in predicting certain cognitive and behavior performance in later life, and their impairments are likely to continue into childhood and even adulthood. These macroscopic topological changes are found to be associated with possible microstructural maturations, such as axonal growth and myelinations. Collectively, this review provides a detailed delineation of the early changes of the baby brains in the graph-theoretical modeling framework, which opens up a new avenue to understand the developmental principles of the connectome. Copyright © 2018. Published by Elsevier Inc.

Learning Analytics for Networked Learning Models

ERIC Educational Resources Information Center

Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

2014-01-01

Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

Development of compositional and contextual communicable congruence in robots by using dynamic neural network models.

PubMed

Park, Gibeom; Tani, Jun

2015-12-01

The current study presents neurorobotics experiments on acquisition of skills for "communicable congruence" with human via learning. A dynamic neural network model which is characterized by its multiple timescale dynamics property was utilized as a neuromorphic model for controlling a humanoid robot. In the experimental task, the humanoid robot was trained to generate specific sequential movement patterns as responding to various sequences of imperative gesture patterns demonstrated by the human subjects by following predefined compositional semantic rules. The experimental results showed that (1) the adopted MTRNN can achieve generalization by learning in the lower feature perception level by using a limited set of tutoring patterns, (2) the MTRNN can learn to extract compositional semantic rules with generalization in its higher level characterized by slow timescale dynamics, (3) the MTRNN can develop another type of cognitive capability for controlling the internal contextual processes as situated to on-going task sequences without being provided with cues for explicitly indicating task segmentation points. The analysis on the dynamic property developed in the MTRNN via learning indicated that the aforementioned cognitive mechanisms were achieved by self-organization of adequate functional hierarchy by utilizing the constraint of the multiple timescale property and the topological connectivity imposed on the network configuration. These results of the current research could contribute to developments of socially intelligent robots endowed with cognitive communicative competency similar to that of human. Copyright © 2015 Elsevier Ltd. All rights reserved.

Development of rostral inferior parietal lobule area functional connectivity from late childhood to early adulthood.

PubMed

Wang, Mengxing; Zhang, Jilei; Dong, Guangheng; Zhang, Hui; Lu, Haifeng; Du, Xiaoxia

2017-06-01

Although the mirror neuron system (MNS) has been extensively studied in monkeys and adult humans, very little is known about its development. Previous studies suggest that the MNS is present by infancy and that the brain and MNS-related cognitive abilities (such as language, empathy, and imitation learning) continue to develop after childhood. In humans, the PFt area of the inferior parietal lobule (IPL) seems to particularly correlate with the functional properties of the PF area in primates, which contains mirror neurons. However, little is known about the functional connectivity (FC) of the PFt area with other brain areas and whether these networks change over time. Here, we investigated the FC development of the PFt area-based network in 59 healthy subjects aged 7-26 years at resting-state to study brain development from late childhood through adolescence to early adulthood. The bilateral PFt showed similar core FC networks, which included the frontal lobe, the cingulate gyri, the insula, the somatosensory cortex, the precuneus, the superior and inferior parietal lobules, the temporal lobe, and the cerebellum posterior lobes. Furthermore, the FC between the left PFt and the left IPL exhibited a significantly positive correlation with age, and the FC between the left PFt and the right postcentral gyrus exhibited a significantly negative correlation with age. In addition, the FC between the right PFt and the right putamen exhibited a significantly negative correlation with age. Our findings suggest that the PFt area-based network develops and is reorganized with age. Copyright © 2017 ISDN. Published by Elsevier Ltd. All rights reserved.

Patterns recognition of electric brain activity using artificial neural networks

NASA Astrophysics Data System (ADS)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

Trans-species learning of cellular signaling systems with bimodal deep belief networks

PubMed Central

Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

2015-01-01

Motivation: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. Results: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These ‘deep learning’ models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. Availability and implementation: The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. Contact: xinghua@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25995230

Trans-species learning of cellular signaling systems with bimodal deep belief networks.

PubMed

Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

2015-09-15

Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These 'deep learning' models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. xinghua@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Dynamics of a Global Zoonotic Research Network Over 33 Years (1980-2012).

PubMed

Hossain, Liaquat; Karimi, Faezeh; Wigand, Rolf T

2015-10-01

The increasing rate of outbreaks in humans of zoonotic diseases requires detailed examination of the education, research, and practice of animal health and its connection to human health. This study investigated the collaboration network of different fields engaged in conducting zoonotic research from a transdisciplinary perspective. Examination of the dynamics of this network for a 33-year period from 1980 to 2012 is presented through the development of a large scientometric database from Scopus. In our analyses we compared several properties of these networks, including density, clustering coefficient, giant component, and centrality measures over time. We also elicited patterns in different fields of study collaborating with various other fields for zoonotic research. We discovered that the strongest collaborations across disciplines are formed among the fields of medicine; biochemistry, genetics, and molecular biology; immunology and microbiology; veterinary; agricultural and biological sciences; and social sciences. Furthermore, the affiliation network is growing overall in terms of collaborative research among different fields of study such that more than two-thirds of all possible collaboration links among disciplines have already been formed. Our findings indicate that zoonotic research scientists in different fields (human or animal health, social science, earth and environmental sciences, engineering) have been actively collaborating with each other over the past 11 years.

Development and application of Human Genome Epidemiology

NASA Astrophysics Data System (ADS)

Xu, Jingwen

2017-12-01

Epidemiology is a science that studies distribution of diseases and health in population and its influencing factors, it also studies how to prevent and cure disease and promote health strategies and measures. Epidemiology has developed rapidly in recent years and it is an intercross subject with various other disciplines to form a series of branch disciplines such as Genetic epidemiology, molecular epidemiology, drug epidemiology and tumor epidemiology. With the implementation and completion of Human Genome Project (HGP), Human Genome Epidemiology (HuGE) has emerged at this historic moment. In this review, the development of Human Genome Epidemiology, research content, the construction and structure of relevant network, research standards, as well as the existing results and problems are briefly outlined.

A Cyber Situational Awareness Model for Network Administrators

DTIC Science & Technology

2017-03-01

environments, the Internet of Things, artificial intelligence , and so on. As users’ data requirements grow more complex, they demand information...security of systems of interest. Further, artificial intelligence is a powerful concept in information technology. Therefore, new research should...look into how to use artificial intelligence to develop CSA. Human interaction with cyber systems is not making networks and their components safer

Nonhumans Unbound: Actor-Network Theory and the Reconsideration of "Things" in Educational Foundations

ERIC Educational Resources Information Center

Waltz, Scott B.

2006-01-01

The aim of this paper is to call attention to the missing discourse of non-humans as social actors in the Social Foundations of Education. The paper outlines three common figuring metaphors that impede the adoption of such a theoretical discourse and shows how Actor-Network Theory (ANT), more recently developed in the nascent field of Science and…

Using Centrality of Concept Maps as a Measure of Problem Space States in Computer-Supported Collaborative Problem Solving

ERIC Educational Resources Information Center

Clariana, Roy B.; Engelmann, Tanja; Yu, Wu

2013-01-01

Problem solving likely involves at least two broad stages, problem space representation and then problem solution (Newell and Simon, Human problem solving, 1972). The metric centrality that Freeman ("Social Networks" 1:215-239, 1978) implemented in social network analysis is offered here as a potential measure of both. This development research…

Development and Optimization of Viable Human Platforms through 3D Printing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parker, Paul R.; Moya, Monica L.; Wheeler, Elizabeth K.

2015-08-21

3D printing technology offers a unique method for creating cell cultures in a manner far more conducive to accurate representation of human tissues and systems. Here we print cellular structures capable of forming vascular networks and exhibiting qualities of natural tissues and human systems. This allows for cheaper and readily available sources for further study of biological and pharmaceutical agents.

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

PubMed Central

Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D.H.; Jo, Brian; Gao, Chuan; McDowell, Ian C.; Engelhardt, Barbara E.

2017-01-01

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. PMID:29021288

Emergent Network Defense

ERIC Educational Resources Information Center

Crane, Earl Newell

2013-01-01

The research problem that inspired this effort is the challenge of managing the security of systems in large-scale heterogeneous networked environments. Human intervention is slow and limited: humans operate at much slower speeds than networked computer communications and there are few humans associated with each network. Enabling each node in the…

Application of heterogeneous pulse coupled neural network in image quantization

NASA Astrophysics Data System (ADS)

Huang, Yi; Ma, Yide; Li, Shouliang; Zhan, Kun

2016-11-01

On the basis of the different strengths of synaptic connections between actual neurons, this paper proposes a heterogeneous pulse coupled neural network (HPCNN) algorithm to perform quantization on images. HPCNNs are developed from traditional pulse coupled neural network (PCNN) models, which have different parameters corresponding to different image regions. This allows pixels of different gray levels to be classified broadly into two categories: background regional and object regional. Moreover, an HPCNN also satisfies human visual characteristics. The parameters of the HPCNN model are calculated automatically according to these categories, and quantized results will be optimal and more suitable for humans to observe. At the same time, the experimental results of natural images from the standard image library show the validity and efficiency of our proposed quantization method.

The game of go as a complex network

NASA Astrophysics Data System (ADS)

Georgeot, Bertrand; Giraud, Olivier; Kandiah, Vivek

2014-03-01

We have studied the game of go, one of the most ancient and complex board games, from a complex network perspective. We have defined a proper categorization of moves taking into account the local environment, and shown that in this case Zipf's law emerges from data taken from real games. The network shows differences between professional and amateur games, different level of amateurs, or different phases of the game. Certain eigenvectors are localized on specific groups of moves which correspond to different strategies (communities of moves). The point of view developed should allow to better modelize such games and could also help to design simulators which could in the future beat good human players. Our approach could be used for other types of games, and in parallel shed light on the human decision making process.

Enhancing Interdisciplinary Human System Risk Research Through Modeling and Network Approaches

NASA Technical Reports Server (NTRS)

Mindock, Jennifer; Lumpkins, Sarah; Shelhamer, Mark

2015-01-01

NASA's Human Research Program (HRP) supports research to reduce human health and performance risks inherent in future human space exploration missions. Understanding risk outcomes and contributing factors in an integrated manner allows HRP research to support development of efficient and effective mitigations from cross-disciplinary perspectives, and to enable resilient human and engineered systems for spaceflight. The purpose of this work is to support scientific collaborations and research portfolio management by utilizing modeling for analysis and visualization of current and potential future interdisciplinary efforts.

CLEANER-Hydrologic Observatory Joint Science Plan

NASA Astrophysics Data System (ADS)

Welty, C.; Dressler, K.; Hooper, R.

2005-12-01

The CLEANER-Hydrologic Observatory* initiative is a distributed network for research on complex environmental systems that focuses on the intersecting water-related issues of both the CUAHSI and CLEANER communities. It emphasizes research on the nation's water resources related to human-dominated natural and built environments. The network will be comprised of: interacting field sites with an integrated cyberinfrastructure; a centralized technical resource staff and management infrastructure to support interdisciplinary research through data collection from advanced sensor systems, data mining and aggregation from multiple sources and databases; cyber-tools for analysis, visualization, and predictive multi-scale modeling that is dynamically driven. As such, the network will transform 21st century workforce development in the water-related intersection of environmental science and engineering, as well as enable substantial educational and engagement opportunities for all age levels. The scientific goal and strategic intent of the CLEANER-Hydrologic Observatory Network is to transform our understanding of the earth's water cycle and associated biogeochemical cycles across spatial and temporal scales-enabling quantitative forecasts of critical water-related processes, especially those that affect and are affected by human activities. This strategy will develop scientific and engineering tools that will enable more effective adaptive approaches for resource management. The need for the network is based on three critical deficiencies in current abilities to understand large-scale environmental processes and thereby develop more effective management strategies. First we lack basic data and the infrastructure to collect them at the needed resolution. Second, we lack the means to integrate data across scales from different media (paper records, electronic worksheets, web-based) and sources (observations, experiments, simulations). Third, we lack sufficiently accurate modeling and decision-support tools to predict the underlying processes or subsequently forecast the effects of different management strategies. Water is a critical driver for the functioning of all ecosystems and development of human society, and it is a key ingredient for the success of industry, agriculture and, national economy. CLEANER-Hydrologic Observatories will foster cutting-edge science and engineering research that addresses major national needs (public and governmental) related to water and include, for example: (i) water resource problems, such as impaired surface waters, contaminated ground water, water availability for human use and ecosystem needs, floods and floodplain management, urban storm water, agricultural runoff, and coastal hypoxia; (ii) understanding environmental impacts on public health; (iii) achieving a balance of economic and environmental sustainability; (iv) reversing environmental degradation; and (v) protecting against chemical and biological threats. CLEANER (Collaborative Large-scale Engineering Analysis Network for Environmental Research) is an ENG initiative; the Hydrologic Observatory Network is GEO initiative through CUAHSI (Consortium of Universities for the Advancement of Hydrologic Science, Inc.). The two initiatives were merged into a joint, bi-directorate program in December 2004.

Human ergology that promotes participatory approach to improving safety, health and working conditions at grassroots workplaces: achievements and actions.

PubMed

Kawakami, Tsuyoshi

2011-12-01

Participatory approaches are increasingly applied to improve safety, health and working conditions of grassroots workplaces in Asia. The core concepts and methods in human ergology research such as promoting real work life studies, relying on positive efforts of local people (daily life-technology), promoting active participation of local people to identify practical solutions, and learning from local human networks to reach grassroots workplaces, have provided useful viewpoints to devise such participatory training programmes. This study was aimed to study and analyze how human ergology approaches were applied in the actual development and application of three typical participatory training programmes: WISH (Work Improvement for Safe Home) with home workers in Cambodia, WISCON (Work Improvement in Small Construction Sites) with construction workers in Thailand, and WARM (Work Adjustment for Recycling and Managing Waste) with waste collectors in Fiji. The results revealed that all the three programmes, in the course of their developments, commonly applied direct observation methods of the work of target workers before devising the training programmes, learned from existing local good examples and efforts, and emphasized local human networks for cooperation. These methods and approaches were repeatedly applied in grassroots workplaces by taking advantage of their the sustainability and impacts. It was concluded that human ergology approaches largely contributed to the developments and expansion of participatory training programmes and could continue to support the self-help initiatives of local people for promoting human-centred work.

The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes

PubMed Central

Galeazzi, Juan M.; Navajas, Joaquín; Mender, Bedeho M. W.; Quian Quiroga, Rodrigo; Minini, Loredana; Stringer, Simon M.

2016-01-01

ABSTRACT Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device. This combination of data allowed us to reconstruct the retinal images seen as humans undertook the jigsaw task. These retinal images were then fed into the neural network model during self-organization of its synaptic connectivity using a biologically plausible trace learning rule. A trace learning mechanism encourages neurons in the model to learn to respond to input images that tend to occur in close temporal proximity. In the data recorded from human subjects, we found that the participant’s gaze often shifted through a sequence of locations around a fixed spatial configuration of the hand and one of the jigsaw pieces. In this case, trace learning should bind these retinal images together onto the same subset of output neurons. The simulation results consequently confirmed that some cells learned to respond selectively to the hand and a jigsaw piece in a fixed spatial configuration across different retinal views. PMID:27253452

The visual development of hand-centered receptive fields in a neural network model of the primate visual system trained with experimentally recorded human gaze changes.

PubMed

Galeazzi, Juan M; Navajas, Joaquín; Mender, Bedeho M W; Quian Quiroga, Rodrigo; Minini, Loredana; Stringer, Simon M

2016-01-01

Neurons have been found in the primate brain that respond to objects in specific locations in hand-centered coordinates. A key theoretical challenge is to explain how such hand-centered neuronal responses may develop through visual experience. In this paper we show how hand-centered visual receptive fields can develop using an artificial neural network model, VisNet, of the primate visual system when driven by gaze changes recorded from human test subjects as they completed a jigsaw. A camera mounted on the head captured images of the hand and jigsaw, while eye movements were recorded using an eye-tracking device. This combination of data allowed us to reconstruct the retinal images seen as humans undertook the jigsaw task. These retinal images were then fed into the neural network model during self-organization of its synaptic connectivity using a biologically plausible trace learning rule. A trace learning mechanism encourages neurons in the model to learn to respond to input images that tend to occur in close temporal proximity. In the data recorded from human subjects, we found that the participant's gaze often shifted through a sequence of locations around a fixed spatial configuration of the hand and one of the jigsaw pieces. In this case, trace learning should bind these retinal images together onto the same subset of output neurons. The simulation results consequently confirmed that some cells learned to respond selectively to the hand and a jigsaw piece in a fixed spatial configuration across different retinal views.

Decoding Lifespan Changes of the Human Brain Using Resting-State Functional Connectivity MRI

PubMed Central

Wang, Lubin; Su, Longfei; Shen, Hui; Hu, Dewen

2012-01-01

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8–79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' “brain ages” from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI. PMID:22952990

Decoding lifespan changes of the human brain using resting-state functional connectivity MRI.

PubMed

Wang, Lubin; Su, Longfei; Shen, Hui; Hu, Dewen

2012-01-01

The development of large-scale functional brain networks is a complex, lifelong process that can be investigated using resting-state functional connectivity MRI (rs-fcMRI). In this study, we aimed to decode the developmental dynamics of the whole-brain functional network in seven decades (8-79 years) of the human lifespan. We first used parametric curve fitting to examine linear and nonlinear age effect on the resting human brain, and then combined manifold learning and support vector machine methods to predict individuals' "brain ages" from rs-fcMRI data. We found that age-related changes in interregional functional connectivity exhibited spatially and temporally specific patterns. During brain development from childhood to senescence, functional connections tended to linearly increase in the emotion system and decrease in the sensorimotor system; while quadratic trajectories were observed in functional connections related to higher-order cognitive functions. The complex patterns of age effect on the whole-brain functional network could be effectively represented by a low-dimensional, nonlinear manifold embedded in the functional connectivity space, which uncovered the inherent structure of brain maturation and aging. Regression of manifold coordinates with age further showed that the manifold representation extracted sufficient information from rs-fcMRI data to make prediction about individual brains' functional development levels. Our study not only gives insights into the neural substrates that underlie behavioral and cognitive changes over age, but also provides a possible way to quantitatively describe the typical and atypical developmental progression of human brain function using rs-fcMRI.

Developing a Client Base for Career Counseling Services.

ERIC Educational Resources Information Center

Werbel, James D.

1988-01-01

The author discusses how to attract clients to a career counseling practice. Topics covered include establishing referral networks with other human resources professionals and using direct marketing. (CH)

[A utopian episode - Carl Friedrich von Weizsäcker in the networks of the Max-Planck Society].

PubMed

Kant, Horst; Renn, Jürgen

2014-01-01

Carl Friedrich von Weizsäcker was a key figure in the history of the Max Planck Society (MPS). This essay contextualises his work with the development of the MPS, highlighting the institutional and personal networks upon which it was based. Some of the stations addressed in the following are his role in the German Uranium Project, in preparing the Mainau Declaration, the Göttingen Manifesto, and the Memorandum of Tübingen as well as his involvement in the foundation of the Max Planck Institute (MPI) for Human Development and his own MPI for the Research of Living Conditions in the Modern World located in Starnberg. The relationship between Weizsäcker and Hellmut Becker, long-time friend and founding director of the MPI for Human Development, will be of particular interest. Another issue broached here is the connection between natural science and the humanities in Weizsäcker's work, and subsequently the relation between these two science cultures in the MPS. Finally, we look at the challenges Weizsäcker's work could present to the MPS today.

Monitoring activities of daily living based on wearable wireless body sensor network.

PubMed

Kańtoch, E; Augustyniak, P; Markiewicz, M; Prusak, D

2014-01-01

With recent advances in microprocessor chip technology, wireless communication, and biomedical engineering it is possible to develop miniaturized ubiquitous health monitoring devices that are capable of recording physiological and movement signals during daily life activities. The aim of the research is to implement and test the prototype of health monitoring system. The system consists of the body central unit with Bluetooth module and wearable sensors: the custom-designed ECG sensor, the temperature sensor, the skin humidity sensor and accelerometers placed on the human body or integrated with clothes and a network gateway to forward data to a remote medical server. The system includes custom-designed transmission protocol and remote web-based graphical user interface for remote real time data analysis. Experimental results for a group of humans who performed various activities (eg. working, running, etc.) showed maximum 5% absolute error compared to certified medical devices. The results are promising and indicate that developed wireless wearable monitoring system faces challenges of multi-sensor human health monitoring during performing daily activities and opens new opportunities in developing novel healthcare services.

Directory of Human Sciences Research Organizations and Professional Associations in South Africa. Second Edition.

ERIC Educational Resources Information Center

van der Berg, Henda, Ed.; Prinsloo, Roelf, Ed.; Pienaar, Drienie, Ed.

This directory is intended to be a comprehensive reference source for identifying research organizations and institutions, and for promoting research cooperation and facilitating networking. This second edition provides a broad background to the development of the human sciences as well as an overview of existing and emerging science and…

Dynamic transcriptional signatures and network responses for clinical symptoms in influenza-infected human subjects using systems biology approaches.

PubMed

Linel, Patrice; Wu, Shuang; Deng, Nan; Wu, Hulin

2014-10-01

Recent studies demonstrate that human blood transcriptional signatures may be used to support diagnosis and clinical decisions for acute respiratory viral infections such as influenza. In this article, we propose to use a newly developed systems biology approach for time course gene expression data to identify significant dynamically response genes and dynamic gene network responses to viral infection. We illustrate the methodological pipeline by reanalyzing the time course gene expression data from a study with healthy human subjects challenged by live influenza virus. We observed clear differences in the number of significant dynamic response genes (DRGs) between the symptomatic and asymptomatic subjects and also identified DRG signatures for symptomatic subjects with influenza infection. The 505 common DRGs shared by the symptomatic subjects have high consistency with the signature genes for predicting viral infection identified in previous works. The temporal response patterns and network response features were carefully analyzed and investigated.

Advanced systems biology methods in drug discovery and translational biomedicine.

PubMed

Zou, Jun; Zheng, Ming-Wu; Li, Gen; Su, Zhi-Guang

2013-01-01

Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.

Novel insights into embryonic stem cell self-renewal revealed through comparative human and mouse systems biology networks.

PubMed

Dowell, Karen G; Simons, Allen K; Bai, Hao; Kell, Braden; Wang, Zack Z; Yun, Kyuson; Hibbs, Matthew A

2014-05-01

Embryonic stem cells (ESCs), characterized by their ability to both self-renew and differentiate into multiple cell lineages, are a powerful model for biomedical research and developmental biology. Human and mouse ESCs share many features, yet have distinctive aspects, including fundamental differences in the signaling pathways and cell cycle controls that support self-renewal. Here, we explore the molecular basis of human ESC self-renewal using Bayesian network machine learning to integrate cell-type-specific, high-throughput data for gene function discovery. We integrated high-throughput ESC data from 83 human studies (~1.8 million data points collected under 1,100 conditions) and 62 mouse studies (~2.4 million data points collected under 1,085 conditions) into separate human and mouse predictive networks focused on ESC self-renewal to analyze shared and distinct functional relationships among protein-coding gene orthologs. Computational evaluations show that these networks are highly accurate, literature validation confirms their biological relevance, and reverse transcriptase polymerase chain reaction (RT-PCR) validation supports our predictions. Our results reflect the importance of key regulatory genes known to be strongly associated with self-renewal and pluripotency in both species (e.g., POU5F1, SOX2, and NANOG), identify metabolic differences between species (e.g., threonine metabolism), clarify differences between human and mouse ESC developmental signaling pathways (e.g., leukemia inhibitory factor (LIF)-activated JAK/STAT in mouse; NODAL/ACTIVIN-A-activated fibroblast growth factor in human), and reveal many novel genes and pathways predicted to be functionally associated with self-renewal in each species. These interactive networks are available online at www.StemSight.org for stem cell researchers to develop new hypotheses, discover potential mechanisms involving sparsely annotated genes, and prioritize genes of interest for experimental validation. © 2013 AlphaMed Press.

A random set scoring model for prioritization of disease candidate genes using protein complexes and data-mining of GeneRIF, OMIM and PubMed records.

PubMed

Jiang, Li; Edwards, Stefan M; Thomsen, Bo; Workman, Christopher T; Guldbrandtsen, Bernt; Sørensen, Peter

2014-09-24

Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Thus, the development of a network-based approach combined with phenotypic profiling would be useful for disease gene prioritization. We developed a random-set scoring model and implemented it to quantify phenotype relevance in a network-based disease gene-prioritization approach. We validated our approach based on different gene phenotypic profiles, which were generated from PubMed abstracts, OMIM, and GeneRIF records. We also investigated the validity of several vocabulary filters and different likelihood thresholds for predicted protein-protein interactions in terms of their effect on the network-based gene-prioritization approach, which relies on text-mining of the phenotype data. Our method demonstrated good precision and sensitivity compared with those of two alternative complex-based prioritization approaches. We then conducted a global ranking of all human genes according to their relevance to a range of human diseases. The resulting accurate ranking of known causal genes supported the reliability of our approach. Moreover, these data suggest many promising novel candidate genes for human disorders that have a complex mode of inheritance. We have implemented and validated a network-based approach to prioritize genes for human diseases based on their phenotypic profile. We have devised a powerful and transparent tool to identify and rank candidate genes. Our global gene prioritization provides a unique resource for the biological interpretation of data from genome-wide association studies, and will help in the understanding of how the associated genetic variants influence disease or quantitative phenotypes.

Is My Network Module Preserved and Reproducible?

PubMed Central

Langfelder, Peter; Luo, Rui; Oldham, Michael C.; Horvath, Steve

2011-01-01

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation. PMID:21283776

Planning Tripoli Metro Network by the Use of Remote Sensing Imagery

NASA Astrophysics Data System (ADS)

Alhusain, O.; Engedy, Gy.; Milady, A.; Paulini, L.; Soos, G.

2012-08-01

Tripoli, the capital city of Libya is going through significant and integrated development process, this development is expected to continue in the next few decades. The Libyan authorities have put it as their goal to develop Tripoli to an important metropolis in North Africa. To achieve this goal, they identified goals for the city's future development in all human, economic, cultural, touristic, and nonetheless infrastructure levels. On the infrastructure development level, among other things, they have identified the development of public transportation as one of the important development priorities. At present, public transportation in Tripoli is carried out by a limited capacity bus network alongside of individual transportation. However, movement in the city is characterized mainly by individual transportation with all its disadvantages such as traffic jams, significant air pollution with both carbon monoxide and dust, and lack of parking space. The Libyan authorities wisely opted for an efficient, modern, and environment friendly solution for public transportation, this was to plan a complex Metro Network as the backbone of public transportation in the city, and to develop and integrate the bus network and other means of transportation to be in harmony with the planned Metro network. The Metro network is planned to provide convenient connections to Tripoli International Airport and to the planned Railway station. They plan to build a system of Park and Ride (P+R) facilities at suitable locations along the Metro lines. This paper will present in details the planned Metro Network, some of the applied technological solutions, the importance of applying remote sensing and GIS technologies in different planning phases, and problems and benefits associated with the use of multi-temporal-, multi-format spatial data in the whole network planning phase.

Micro/Nanosatellite Mars Network for Global Lower Atmosphere Characterization

NASA Technical Reports Server (NTRS)

Tinker, Mike L.

2012-01-01

To address multiple key challenge areas for robotic exploration of Mars, to achieve scientific goals and reduce risk for future human missions, a micro/nanosatellite constellation for lower atmosphere characterization is proposed. A microsatellite design is discussed that can operate (1) in tandem with another microsat or (2) as a "mother-ship" to deploy a network of nanosatellites (CubeSats). Either configuration of the network would perform radio occultation-based atmospheric measurements. Advantages of the proposed network are low development cost based on an existing microsatellite bus, and proven performance of the bus to date. Continued efforts in miniaturization of instruments are needed to fully enable the mother-ship/nanosat version of the proposed network.

Angiogenesis assays.

PubMed

Mydlo, J H

2001-01-01

Angiogenesis-the formation of a vascular network-is essential for the support of a developing tumor when simple diffusion of nutrients is impossible. The ability of a solid tumor to achieve metabolic needs beyond simple diffusion is dependent on the development of this neovascular network. The process of angiogenesis lets the tumor become self-sufficient to grow, and also gives it the ability to metastasize. Growth factors added to human-vein endothelial cells in culture may demonstrate tubularization of cells, but this does not necessarily imply angiogenesis. True in vivo angiogenesis means not only the mobilization of endothelial cells, but the degradation of the matrix and the formation of vessel sprouts in a network that can transport red blood cells (RBCs).

Human Impacts and Climate Change Influence Nestedness and Modularity in Food-Web and Mutualistic Networks.

PubMed

Takemoto, Kazuhiro; Kajihara, Kosuke

2016-01-01

Theoretical studies have indicated that nestedness and modularity-non-random structural patterns of ecological networks-influence the stability of ecosystems against perturbations; as such, climate change and human activity, as well as other sources of environmental perturbations, affect the nestedness and modularity of ecological networks. However, the effects of climate change and human activities on ecological networks are poorly understood. Here, we used a spatial analysis approach to examine the effects of climate change and human activities on the structural patterns of food webs and mutualistic networks, and found that ecological network structure is globally affected by climate change and human impacts, in addition to current climate. In pollination networks, for instance, nestedness increased and modularity decreased in response to increased human impacts. Modularity in seed-dispersal networks decreased with temperature change (i.e., warming), whereas food web nestedness increased and modularity declined in response to global warming. Although our findings are preliminary owing to data-analysis limitations, they enhance our understanding of the effects of environmental change on ecological communities.

Inspiration from heart development: Biomimetic development of functional human cardiac organoids.

PubMed

Richards, Dylan J; Coyle, Robert C; Tan, Yu; Jia, Jia; Wong, Kerri; Toomer, Katelynn; Menick, Donald R; Mei, Ying

2017-10-01

Recent progress in human organoids has provided 3D tissue systems to model human development, diseases, as well as develop cell delivery systems for regenerative therapies. While direct differentiation of human embryoid bodies holds great promise for cardiac organoid production, intramyocardial cell organization during heart development provides biological foundation to fabricate human cardiac organoids with defined cell types. Inspired by the intramyocardial organization events in coronary vasculogenesis, where a diverse, yet defined, mixture of cardiac cell types self-organizes into functional myocardium in the absence of blood flow, we have developed a defined method to produce scaffold-free human cardiac organoids that structurally and functionally resembled the lumenized vascular network in the developing myocardium, supported hiPSC-CM development and possessed fundamental cardiac tissue-level functions. In particular, this development-driven strategy offers a robust, tunable system to examine the contributions of individual cell types, matrix materials and additional factors for developmental insight, biomimetic matrix composition to advance biomaterial design, tissue/organ-level drug screening, and cell therapy for heart repair. Copyright © 2017 Elsevier Ltd. All rights reserved.

Enzymatic regulation of functional vascular networks using gelatin hydrogels

PubMed Central

Chuang, Chia-Hui; Lin, Ruei-Zeng; Tien, Han-Wen; Chu, Ya-Chun; Li, Yen-Cheng; Melero-Martin, Juan M.; Chen, Ying-Chieh

2015-01-01

To manufacture tissue engineering-based functional tissues, scaffold materials that can be sufficiently vascularized to mimic the functionality and complexity of native tissues are needed. Currently, vascular network bioengineering is largely carried out using natural hydrogels as embedding scaffolds, but most natural hydrogels have poor mechanical stability and durability, factors that critically limit their widespread use. In this study, we examined the suitability of gelatin-phenolic hydroxyl (gelatin-Ph) hydrogels that can be enzymatically crosslinked, allowing tuning of the storage modulus and the proteolytic degradation rate, for use as injectable hydrogels to support the human progenitor cell-based formation of a stable and mature vascular network. Porcine gelatin-Ph hydrogels were found to be cytocompatible with human blood-derived endothelial colony-forming cells and white adipose tissue-derived mesenchymal stem cells, resulting in >87% viability, and cell proliferation and spreading could be modulated by using hydrogels with different proteolytic degradability and stiffness. In addition, gelatin was extracted from mouse dermis and murine gelatin-Ph hydrogels were prepared. Importantly, implantation of human cell-laden porcine or murine gelatin-Ph hydrogels into immunodeficient mice resulted in the rapid formation of functional anastomoses between the bioengineered human vascular network and the mouse vasculature. Furthermore, the degree of enzymatic crosslinking of the gelatin-Ph hydrogels could be used to modulate cell behavior and the extent of vascular network formation in vivo. Our report details a technique for the synthesis of gelatin-Ph hydrogels from allogeneic or xenogeneic dermal skin and suggests that these hydrogels can be used for biomedical applications that require the formation of microvascular networks, including the development of complex engineered tissues. PMID:25749296

Management of space networks

NASA Technical Reports Server (NTRS)

Markley, R. W.; Williams, B. F.

1993-01-01

NASA has proposed missions to the Moon and Mars that reflect three areas of emphasis: human presence, exploration, and space resource development for the benefit of Earth. A major requirement for such missions is a robust and reliable communications architecture. Network management--the ability to maintain some degree of human and automatic control over the span of the network from the space elements to the end users on Earth--is required to realize such robust and reliable communications. This article addresses several of the architectural issues associated with space network management. Round-trip delays, such as the 5- to 40-min delays in the Mars case, introduce a host of problems that must be solved by delegating significant control authority to remote nodes. Therefore, management hierarchy is one of the important architectural issues. The following article addresses these concerns, and proposes a network management approach based on emerging standards that covers the needs for fault, configuration, and performance management, delegated control authority, and hierarchical reporting of events. A relatively simple approach based on standards was demonstrated in the DSN 2000 Information Systems Laboratory, and the results are described.

Operational problems of Haniwa net as a form of social capital: interdependence between human networks of physicians and information networks.

PubMed

Maeda, Minoru; Araki, Sanae; Suzuki, Muneou; Umemoto, Katsuhiro; Kai, Yukiko; Araki, Kenji

2012-10-01

In August 2009, Miyazaki Health and Welfare Network (Haniwa Net, hereafter referred to as "the Net"), centrally led by University of Miyazaki Hospital (UMH), adopted a center hospital-based system offering a unilateral linkage that enables the viewing of UMH's medical records through a web-based browser (electronic medical records (EMR)). By the end of December 2010, the network had developed into a system of 79 collaborating physicians from within the prefecture. Beginning in August 2010, physicians in 12 medical institutions were visited and asked to speak freely on the operational issues concerning the Net. Recordings and written accounts were coded using the text analysis software MAXQDA 10 to understand the actual state of operations. Analysis of calculations of Kendall's rank correlation confirmed that the interdependency between human networks and information networks is significant. At the same time, while the negative opinions concerning the functions of the Net were somewhat conspicuous, the results showed a correlation between requests and proposals for operational improvements of the Net, clearly indicating the need for a more user-friendly system and a better viewer.

Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain.

PubMed

Cao, Miao; He, Yong; Dai, Zhengjia; Liao, Xuhong; Jeon, Tina; Ouyang, Minhui; Chalak, Lina; Bi, Yanchao; Rollins, Nancy; Dong, Qi; Huang, Hao

2017-03-01

Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small-worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting-state functional MRI and voxel-based graph theory analysis to systematically investigate the topological organization of whole-brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high-order default-mode and executive-control regions. The hub and rich-club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Evolution of Osteocrin as an activity-regulated factor in the primate brain

PubMed Central

Ataman, Bulent; Boulting, Gabriella L.; Harmin, David A.; Yang, Marty G.; Baker-Salisbury, Mollie; Yap, Ee-Lynn; Malik, Athar N.; Mei, Kevin; Rubin, Alex A.; Spiegel, Ivo; Durresi, Ershela; Sharma, Nikhil; Hu, Linda S.; Pletikos, Mihovil; Griffith, Eric C.; Partlow, Jennifer N.; Stevens, Christine R.; Adli, Mazhar; Chahrour, Maria; Sestan, Nenad; Walsh, Christopher A.; Berezovskii, Vladimir K.; Livingstone, Margaret S.; Greenberg, Michael E.

2017-01-01

Sensory stimuli drive the maturation and function of the mammalian nervous system in part through the activation of gene expression networks that regulate synapse development and plasticity. These networks have primarily been studied in mice, and it is not known whether there are species- or clade-specific activity-regulated genes that control features of brain development and function. Here we use transcriptional profiling of human fetal brain cultures to identify an activity-dependent secreted factor, Osteocrin (OSTN), that is induced by membrane depolarization of human but not mouse neurons. We find that OSTN has been repurposed in primates through the evolutionary acquisition of DNA regulatory elements that bind the activity-regulated transcription factor MEF2. In addition, we demonstrate that OSTN is expressed in primate neocortex and restricts activity-dependent dendritic growth in human neurons. These findings suggest that, in response to sensory input, OSTN regulates features of neuronal structure and function that are unique to primates. PMID:27830782

Thinking Like a Human

NASA Technical Reports Server (NTRS)

1999-01-01

Accurate Automation Corporation (AAC) of Chattanooga, TN, developed a neural network processor (NNP) for use onboard the NASA- and Air Force-sponsored LoFLYTE aircraft. The processor is modeled after connections in the brain.

Globalisation and human dignity: the case of the information superhighway.

PubMed

Hamelink, C J

1996-01-01

In 1994, Vice President Al Gore coined the concept of the Information Superhighway during a speech in Buenos Aires in which he proposed the development of a global information infrastructure. The project envisions the incorporation of all existing communication networks into one system, facilitating the globalization of markets. The internet, a decentralized network of computer networks, is one small-scale, existing model of what the superhighway could be, a public space owned by nobody in which communication takes place largely for noncommercial purposes. The internet currently connects 40 million computer users from at least 90 countries. The alternative model for the superhighway is the privately managed global shopping mall, a global interactive marketplace for information, entertainment, and advertising. The author warns, however, that it is not possible to predict the future social impact of such an information superhighway. Decision makers nonetheless push ahead in development, full of confidence in the merit and infallibility of technology. Since one cannot accurately predict the future, it rational to assume that social consequences may both promote and threaten human dignity. The author explains at which level he believes the superhighway threatens the human rights norms of equality, inviolability, and liberty, and discusses what the World Association for Christian Communication can do.

Assessment of the Contribution of Regional Higher Education Systems to the Socio-Economic Development of the Russian Regions

ERIC Educational Resources Information Center

Leshukov, O. V.; Yevseyeva, D. G.; Gromov, A. D.; Platonova, D. P.

2017-01-01

This article analyzes how Russia's networks of higher education institutions contribute to their host regions in terms of the following three major facets: the economic development; the human capital development; and the innovative development. To ensure the analytical framework used derives relevant and representative findings given the nature of…

Interconnectivity of human cellular metabolism and disease prevalence

NASA Astrophysics Data System (ADS)

Lee, Deok-Sun

2010-12-01

Fluctuations of metabolic reaction fluxes may cause abnormal concentrations of toxic or essential metabolites, possibly leading to metabolic diseases. The mutual binding of enzymatic proteins and ones involving common metabolites enforces distinct coupled reactions, by which local perturbations may spread through the cellular network. Such network effects at the molecular interaction level in human cellular metabolism can reappear in the patterns of disease occurrence. Here we construct the enzyme-reaction network and the metabolite-reaction network, capturing the flux coupling of metabolic reactions caused by the interacting enzymes and the shared metabolites, respectively. Diseases potentially caused by the failure of individual metabolic reactions can be identified by using the known disease-gene association, which allows us to derive the probability of an inactivated reaction causing diseases from the disease records at the population level. We find that the greater the number of proteins that catalyze a reaction, the higher the mean prevalence of its associated diseases. Moreover, the number of connected reactions and the mean size of the avalanches in the networks constructed are also shown to be positively correlated with the disease prevalence. These findings illuminate the impact of the cellular network topology on disease development, suggesting that the global organization of the molecular interaction network should be understood to assist in disease diagnosis, treatment, and drug discovery.

Research of the key technology in satellite communication networks

NASA Astrophysics Data System (ADS)

Zeng, Yuan

2018-02-01

According to the prediction, in the next 10 years the wireless data traffic will be increased by 500-1000 times. Not only the wireless data traffic will be increased exponentially, and the demand for diversified traffic will be increased. Higher requirements for future mobile wireless communication system had brought huge market space for satellite communication system. At the same time, the space information networks had been greatly developed with the depth of human exploration of space activities, the development of space application, the expansion of military and civilian application. The core of spatial information networks is the satellite communication. The dissertation presented the communication system architecture, the communication protocol, the routing strategy, switch scheduling algorithm and the handoff strategy based on the satellite communication system. We built the simulation platform of the LEO satellites networks and simulated the key technology using OPNET.

A complex regulatory network coordinating cell cycles during C. elegans development is revealed by a genome-wide RNAi screen.

PubMed

Roy, Sarah H; Tobin, David V; Memar, Nadin; Beltz, Eleanor; Holmen, Jenna; Clayton, Joseph E; Chiu, Daniel J; Young, Laura D; Green, Travis H; Lubin, Isabella; Liu, Yuying; Conradt, Barbara; Saito, R Mako

2014-02-28

The development and homeostasis of multicellular animals requires precise coordination of cell division and differentiation. We performed a genome-wide RNA interference screen in Caenorhabditis elegans to reveal the components of a regulatory network that promotes developmentally programmed cell-cycle quiescence. The 107 identified genes are predicted to constitute regulatory networks that are conserved among higher animals because almost half of the genes are represented by clear human orthologs. Using a series of mutant backgrounds to assess their genetic activities, the RNA interference clones displaying similar properties were clustered to establish potential regulatory relationships within the network. This approach uncovered four distinct genetic pathways controlling cell-cycle entry during intestinal organogenesis. The enhanced phenotypes observed for animals carrying compound mutations attest to the collaboration between distinct mechanisms to ensure strict developmental regulation of cell cycles. Moreover, we characterized ubc-25, a gene encoding an E2 ubiquitin-conjugating enzyme whose human ortholog, UBE2Q2, is deregulated in several cancers. Our genetic analyses suggested that ubc-25 acts in a linear pathway with cul-1/Cul1, in parallel to pathways employing cki-1/p27 and lin-35/pRb to promote cell-cycle quiescence. Further investigation of the potential regulatory mechanism demonstrated that ubc-25 activity negatively regulates CYE-1/cyclin E protein abundance in vivo. Together, our results show that the ubc-25-mediated pathway acts within a complex network that integrates the actions of multiple molecular mechanisms to control cell cycles during development. Copyright © 2014 Roy et al.

A survey of body sensor networks.

PubMed

Lai, Xiaochen; Liu, Quanli; Wei, Xin; Wang, Wei; Zhou, Guoqiao; Han, Guangyi

2013-04-24

The technology of sensor, pervasive computing, and intelligent information processing is widely used in Body Sensor Networks (BSNs), which are a branch of wireless sensor networks (WSNs). BSNs are playing an increasingly important role in the fields of medical treatment, social welfare and sports, and are changing the way humans use computers. Existing surveys have placed emphasis on the concept and architecture of BSNs, signal acquisition, context-aware sensing, and system technology, while this paper will focus on sensor, data fusion, and network communication. And we will introduce the research status of BSNs, the analysis of hotspots, and future development trends, the discussion of major challenges and technical problems facing currently. The typical research projects and practical application of BSNs are introduced as well. BSNs are progressing along the direction of multi-technology integration and intelligence. Although there are still many problems, the future of BSNs is fundamentally promising, profoundly changing the human-machine relationships and improving the quality of people's lives.

A Survey of Body Sensor Networks

PubMed Central

Lai, Xiaochen; Liu, Quanli; Wei, Xin; Wang, Wei; Zhou, Guoqiao; Han, Guangyi

2013-01-01

The technology of sensor, pervasive computing, and intelligent information processing is widely used in Body Sensor Networks (BSNs), which are a branch of wireless sensor networks (WSNs). BSNs are playing an increasingly important role in the fields of medical treatment, social welfare and sports, and are changing the way humans use computers. Existing surveys have placed emphasis on the concept and architecture of BSNs, signal acquisition, context-aware sensing, and system technology, while this paper will focus on sensor, data fusion, and network communication. And we will introduce the research status of BSNs, the analysis of hotspots, and future development trends, the discussion of major challenges and technical problems facing currently. The typical research projects and practical application of BSNs are introduced as well. BSNs are progressing along the direction of multi-technology integration and intelligence. Although there are still many problems, the future of BSNs is fundamentally promising, profoundly changing the human-machine relationships and improving the quality of people's lives. PMID:23615581

[Medical IT-ization and development of pharmacists business].

PubMed

Miwa, Ryouju

2014-01-01

Two major trends are materializing: the super-aging of society and information technology (IT-ization). Thus, the most important action to benefit patients and society is to establish a medical information network supported by a trustworthy human network. This network should be organized by the people involved in local community healthcare. As such, it is essential for the human network to include not only hospitals and clinics (medical practitioners) but also community pharmacies (pharmacists). This need is apparent, because in the past, drug hazards recurred because fundamental improvement of the means to prevent those hazards was not possible without pharmacists where and when those incidences occurred. The medical information to be IT-ized would include drug notebooks and prescription cards, although electronic medical charts will be the ultimate source of information. The following points will be discussed in this paper: (a) Legal requirements for physical assessments by pharmacists, (b) Physical assessments by hospital pharmacists, (c) Physical assessments by community pharmacists, and (d) Security requirements for the Act for Protection of Personal Information, Articles 20-22.

Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease.

PubMed

Johnson, Michael R; Shkura, Kirill; Langley, Sarah R; Delahaye-Duriez, Andree; Srivastava, Prashant; Hill, W David; Rackham, Owen J L; Davies, Gail; Harris, Sarah E; Moreno-Moral, Aida; Rotival, Maxime; Speed, Doug; Petrovski, Slavé; Katz, Anaïs; Hayward, Caroline; Porteous, David J; Smith, Blair H; Padmanabhan, Sandosh; Hocking, Lynne J; Starr, John M; Liewald, David C; Visconti, Alessia; Falchi, Mario; Bottolo, Leonardo; Rossetti, Tiziana; Danis, Bénédicte; Mazzuferi, Manuela; Foerch, Patrik; Grote, Alexander; Helmstaedter, Christoph; Becker, Albert J; Kaminski, Rafal M; Deary, Ian J; Petretto, Enrico

2016-02-01

Genetic determinants of cognition are poorly characterized, and their relationship to genes that confer risk for neurodevelopmental disease is unclear. Here we performed a systems-level analysis of genome-wide gene expression data to infer gene-regulatory networks conserved across species and brain regions. Two of these networks, M1 and M3, showed replicable enrichment for common genetic variants underlying healthy human cognitive abilities, including memory. Using exome sequence data from 6,871 trios, we found that M3 genes were also enriched for mutations ascertained from patients with neurodevelopmental disease generally, and intellectual disability and epileptic encephalopathy in particular. M3 consists of 150 genes whose expression is tightly developmentally regulated, but which are collectively poorly annotated for known functional pathways. These results illustrate how systems-level analyses can reveal previously unappreciated relationships between neurodevelopmental disease-associated genes in the developed human brain, and provide empirical support for a convergent gene-regulatory network influencing cognition and neurodevelopmental disease.

Machine Learning and Quantum Mechanics

NASA Astrophysics Data System (ADS)

Chapline, George

The author has previously pointed out some similarities between selforganizing neural networks and quantum mechanics. These types of neural networks were originally conceived of as away of emulating the cognitive capabilities of the human brain. Recently extensions of these networks, collectively referred to as deep learning networks, have strengthened the connection between self-organizing neural networks and human cognitive capabilities. In this note we consider whether hardware quantum devices might be useful for emulating neural networks with human-like cognitive capabilities, or alternatively whether implementations of deep learning neural networks using conventional computers might lead to better algorithms for solving the many body Schrodinger equation.

Mapping the Structure and Dynamics of Genomics-Related MeSH Terms Complex Networks

PubMed Central

Siqueiros-García, Jesús M.; Hernández-Lemus, Enrique; García-Herrera, Rodrigo; Robina-Galatas, Andrea

2014-01-01

It has been proposed that the history and evolution of scientific ideas may reflect certain aspects of the underlying socio-cognitive frameworks in which science itself is developing. Systematic analyses of the development of scientific knowledge may help us to construct models of the collective dynamics of science. Aiming at scientific rigor, these models should be built upon solid empirical evidence, analyzed with formal tools leading to ever-improving results that support the related conclusions. Along these lines we studied the dynamics and structure of the development of research in genomics as represented by the entire collection of genomics-related scientific papers contained in the PubMed database. The analyzed corpus consisted in more than 49,000 articles published in the years 1987 (first appeareance of the term Genomics) to 2011, categorized by means of the Medical Subheadings (MeSH) content-descriptors. Complex networks were built where two MeSH terms were connected if they are descriptors of the same article(s). The analysis of such networks revealed a complex structure and dynamics that to certain extent resembled small-world networks. The evolution of such networks in time reflected interesting phenomena in the historical development of genomic research, including what seems to be a phase-transition in a period marked by the completion of the first draft of the Human Genome Project. We also found that different disciplinary areas have different dynamic evolution patterns in their MeSH connectivity networks. In the case of areas related to science, changes in topology were somewhat fast while retaining a certain core-stucture, whereas in the humanities, the evolution was pretty slow and the structure resulted highly redundant and in the case of technology related issues, the evolution was very fast and the structure remained tree-like with almost no overlapping terms. PMID:24699262

Development and Implementation of Kumamoto Technopolis Regional Database T-KIND

NASA Astrophysics Data System (ADS)

Onoue, Noriaki

T-KIND (Techno-Kumamoto Information Network for Data-Base) is a system for effectively searching information of technology, human resources and industries which are necessary to realize Kumamoto Technopolis. It is composed of coded database, image database and LAN inside technoresearch park which is the center of R & D in the Technopolis. It constructs on-line system by networking general-purposed computers, minicomputers, optical disk file systems and so on, and provides the service through public telephone line. Two databases are now available on enterprise information and human resource information. The former covers about 4,000 enterprises, and the latter does about 2,000 persons.

Co-expression networks reveal the tissue-specific regulation of transcription and splicing.

PubMed

Saha, Ashis; Kim, Yungil; Gewirtz, Ariel D H; Jo, Brian; Gao, Chuan; McDowell, Ian C; Engelhardt, Barbara E; Battle, Alexis

2017-11-01

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues. © 2017 Saha et al.; Published by Cold Spring Harbor Laboratory Press.

Connecting the dots between genes, biochemistry, and disease susceptibility: systems biology modeling in human genetics.

PubMed

Moore, Jason H; Boczko, Erik M; Summar, Marshall L

2005-02-01

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two or more DNA sequence variations. We review here this approach and then discuss how it can be used to model biochemical and metabolic data in the context of genetic studies of human disease susceptibility.

Fuzzy Modelling for Human Dynamics Based on Online Social Networks

PubMed Central

Cuenca-Jara, Jesus; Valdes-Vela, Mercedes; Skarmeta, Antonio F.

2017-01-01

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities. PMID:28837120

Signal transmission in a human body medium-based body sensor network using a Mach-Zehnder electro-optical sensor.

PubMed

Song, Yong; Hao, Qun; Zhang, Kai; Wang, Jingwen; Jin, Xuefeng; Sun, He

2012-11-30

The signal transmission technology based on the human body medium offers significant advantages in Body Sensor Networks (BSNs) used for healthcare and the other related fields. In previous works we have proposed a novel signal transmission method based on the human body medium using a Mach-Zehnder electro-optical (EO) sensor. In this paper, we present a signal transmission system based on the proposed method, which consists of a transmitter, a Mach-Zehnder EO sensor and a corresponding receiving circuit. Meanwhile, in order to verify the frequency response properties and determine the suitable parameters of the developed system, in-vivo measurements have been implemented under conditions of different carrier frequencies, baseband frequencies and signal transmission paths. Results indicate that the proposed system will help to achieve reliable and high speed signal transmission of BSN based on the human body medium.

Signal Transmission in a Human Body Medium-Based Body Sensor Network Using a Mach-Zehnder Electro-Optical Sensor

PubMed Central

Song, Yong; Hao, Qun; Zhang, Kai; Wang, Jingwen; Jin, Xuefeng; Sun, He

2012-01-01

The signal transmission technology based on the human body medium offers significant advantages in Body Sensor Networks (BSNs) used for healthcare and the other related fields. In previous works we have proposed a novel signal transmission method based on the human body medium using a Mach-Zehnder electro-optical (EO) sensor. In this paper, we present a signal transmission system based on the proposed method, which consists of a transmitter, a Mach-Zehnder EO sensor and a corresponding receiving circuit. Meanwhile, in order to verify the frequency response properties and determine the suitable parameters of the developed system, in-vivo measurements have been implemented under conditions of different carrier frequencies, baseband frequencies and signal transmission paths. Results indicate that the proposed system will help to achieve reliable and high speed signal transmission of BSN based on the human body medium. PMID:23443393

Fuzzy Modelling for Human Dynamics Based on Online Social Networks.

PubMed

Cuenca-Jara, Jesus; Terroso-Saenz, Fernando; Valdes-Vela, Mercedes; Skarmeta, Antonio F

2017-08-24

Human mobility mining has attracted a lot of attention in the research community due to its multiple implications in the provisioning of innovative services for large metropolises. In this scope, Online Social Networks (OSN) have arisen as a promising source of location data to come up with new mobility models. However, the human nature of this data makes it rather noisy and inaccurate. In order to deal with such limitations, the present work introduces a framework for human mobility mining based on fuzzy logic. Firstly, a fuzzy clustering algorithm extracts the most active OSN areas at different time periods. Next, such clusters are the building blocks to compose mobility patterns. Furthermore, a location prediction service based on a fuzzy rule classifier has been developed on top of the framework. Finally, both the framework and the predictor has been tested with a Twitter and Flickr dataset in two large cities.

Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network.

PubMed

Podder, Avijit; Jatana, Nidhi; Latha, N

2014-09-21

Dopamine receptors (DR) are one of the major neurotransmitter receptors present in human brain. Malfunctioning of these receptors is well established to trigger many neurological and psychiatric disorders. Taking into consideration that proteins function collectively in a network for most of the biological processes, the present study is aimed to depict the interactions between all dopamine receptors following a systems biology approach. To capture comprehensive interactions of candidate proteins associated with human dopamine receptors, we performed a protein-protein interaction network (PPIN) analysis of all five receptors and their protein partners by mapping them into human interactome and constructed a human Dopamine Receptors Interaction Network (DRIN). We explored the topology of dopamine receptors as molecular network, revealing their characteristics and the role of central network elements. More to the point, a sub-network analysis was done to determine major functional clusters in human DRIN that govern key neurological pathways. Besides, interacting proteins in a pathway were characterized and prioritized based on their affinity for utmost drug molecules. The vulnerability of different networks to the dysfunction of diverse combination of components was estimated under random and direct attack scenarios. To the best of our knowledge, the current study is unique to put all five dopamine receptors together in a common interaction network and to understand the functionality of interacting proteins collectively. Our study pinpointed distinctive topological and functional properties of human dopamine receptors that have helped in identifying potential therapeutic drug targets in the dopamine interaction network. Copyright © 2014 Elsevier Ltd. All rights reserved.

Neural Network Machine Learning and Dimension Reduction for Data Visualization

NASA Technical Reports Server (NTRS)

Liles, Charles A.

2014-01-01

Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets.

Discovering Network Structure Beyond Communities

NASA Astrophysics Data System (ADS)

Nishikawa, Takashi; Motter, Adilson E.

2011-11-01

To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

Weighting for sex acts to understand the spread of STI on networks.

PubMed

Moslonka-Lefebvre, Mathieu; Bonhoeffer, Sebastian; Alizon, Samuel

2012-10-21

Human sexual networks exhibit a heterogeneous structure where few individuals have many partners and many individuals have few partners. Network theory predicts that the spread of sexually transmitted infections (STI) on such networks should exhibit striking properties (e.g. rapid spread). However, these properties cannot be found in epidemiological data. Current network models typically assume a constant STI transmission risk per partnership, which is unrealistic because it implies that sexual activity is proportional to the number of partners and that individuals have the same activity with each partner. We develop a framework that allows us to weight any sexual network based on biological assumptions. Our results indicate that STI spreading on the resulting weighted networks do not have heterogeneous-related properties, which is consistent with data and earlier studies. Copyright © 2012 Elsevier Ltd. All rights reserved.

Agricultural Students and Their Academic Performance: A Study of Students in Punjab, Pakistan

ERIC Educational Resources Information Center

Arshad, Muhammad; Ullah, Muhammad I.; Mehmood, Muhammad U.; Arshad, Muhammad R.; Khan, Muhammad F.

2016-01-01

Reading is considered as essential to develop the personality of human being. However, reading habits of human being especially young generation is at decline due to initiation of modern digital technologies like mobile phones, social networking on internet along with other ways of different entertainment. The study was conducted to assess the…

The Development, Validity, and Reliability of Communication Satisfaction in an Online Asynchronous Discussion Scale

ERIC Educational Resources Information Center

Hung, Min-Ling; Chou, Chien

2014-01-01

The purpose of this study was to identify dimensions of students' communication satisfaction in an asynchronous discussion forum. An asynchronous discussion may be defined as text-based human-to-human communication via computer networks that provides a platform for the participants to interact with one another to exchange ideas, insights, and…

Implementation of medical monitor system based on networks

NASA Astrophysics Data System (ADS)

Yu, Hui; Cao, Yuzhen; Zhang, Lixin; Ding, Mingshi

2006-11-01

In this paper, the development trend of medical monitor system is analyzed and portable trend and network function become more and more popular among all kinds of medical monitor devices. The architecture of medical network monitor system solution is provided and design and implementation details of medical monitor terminal, monitor center software, distributed medical database and two kind of medical information terminal are especially discussed. Rabbit3000 system is used in medical monitor terminal to implement security administration of data transfer on network, human-machine interface, power management and DSP interface while DSP chip TMS5402 is used in signal analysis and data compression. Distributed medical database is designed for hospital center according to DICOM information model and HL7 standard. Pocket medical information terminal based on ARM9 embedded platform is also developed to interactive with center database on networks. Two kernels based on WINCE are customized and corresponding terminal software are developed for nurse's routine care and doctor's auxiliary diagnosis. Now invention patent of the monitor terminal is approved and manufacture and clinic test plans are scheduled. Applications for invention patent are also arranged for two medical information terminals.

NATO Human View Architecture and Human Networks

NASA Technical Reports Server (NTRS)

Handley, Holly A. H.; Houston, Nancy P.

2010-01-01

The NATO Human View is a system architectural viewpoint that focuses on the human as part of a system. Its purpose is to capture the human requirements and to inform on how the human impacts the system design. The viewpoint contains seven static models that include different aspects of the human element, such as roles, tasks, constraints, training and metrics. It also includes a Human Dynamics component to perform simulations of the human system under design. One of the static models, termed Human Networks, focuses on the human-to-human communication patterns that occur as a result of ad hoc or deliberate team formation, especially teams distributed across space and time. Parameters of human teams that effect system performance can be captured in this model. Human centered aspects of networks, such as differences in operational tempo (sense of urgency), priorities (common goal), and team history (knowledge of the other team members), can be incorporated. The information captured in the Human Network static model can then be included in the Human Dynamics component so that the impact of distributed teams is represented in the simulation. As the NATO militaries transform to a more networked force, the Human View architecture is an important tool that can be used to make recommendations on the proper mix of technological innovations and human interactions.

The IUR Forum: Worldwide Harmonisation of Networks to Support Integration of Scientific Knowledge and Consensus Development in Radioecology.

PubMed

Bréchignac, F; Alexakhin, R; Bollhöfer, A; Frogg, K E; Hardeman, F; Higley, K; Hinton, T G; Kapustka, L A; Kuhne, W; Leonard, K; Masson, O; Nanba, K; Smith, G; Smith, K; Strand, P; Vandenhove, H; Yankovich, T; Yoshida, S

2017-04-01

During the past decades, many specialised networks have formed to meet specific radioecological objectives, whether regional or sectorial (purpose-oriented). Regional networks deal with an array of radioecological issues related to their territories. Examples include the South Pacific network of radioecologists, and the European network of excellence in radioecology. The latter is now part of the European platform for radiation protection. Sectorial networks are more problem-oriented, often with wider international representativeness, but restricted to one specific issue, (e.g. radioactive waste, low-level atmospheric contamination, modelling). All such networks, while often working in relative isolation, contribute to a flow of scientific information which, through United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR's) efforts of synthesis, feeds into the radiation protection frameworks of protecting humans and the environment. The IUR has therefore prompted a co-construction process aimed at improving worldwide harmonisation of radioecology networks. An initiative based on an initial set of 15 networks, now called the IUR FORUM, was launched in June 2014. The IUR Forum agreed to build a framework for improved coordination of scientific knowledge, integration and consensus development relative to environmental radioactivity. Three objectives have been collectively assigned to the IUR FORUM: (1) coordination, (2) global integration and construction of consensus and (3) maintenance of expertise. One particular achievement of the FORUM was an improved description and common understanding of the respective roles and functions of the various networks within the overall scene of radioecology R&D. It clarifies how the various networks assembled within the IUR FORUM interface with UNSCEAR and other international regulatory bodies (IAEA, ICRP), and how consensus on the assessment of risk is constructed. All these agencies interact with regional networks covering different geographical areas, and with other networks which address specific topics within radiation protection. After holding its first Consensus Symposium in 2015, examining the possible ecological impact of radiation from environmental contamination, the IUR FORUM continues its work towards improved radiation protection of humans and the environment. We welcome new members. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Language Networks as Models of Cognition: Understanding Cognition through Language

NASA Astrophysics Data System (ADS)

Beckage, Nicole M.; Colunga, Eliana

Language is inherently cognitive and distinctly human. Separating the object of language from the human mind that processes and creates language fails to capture the full language system. Linguistics traditionally has focused on the study of language as a static representation, removed from the human mind. Network analysis has traditionally been focused on the properties and structure that emerge from network representations. Both disciplines could gain from looking at language as a cognitive process. In contrast, psycholinguistic research has focused on the process of language without committing to a representation. However, by considering language networks as approximations of the cognitive system we can take the strength of each of these approaches to study human performance and cognition as related to language. This paper reviews research showcasing the contributions of network science to the study of language. Specifically, we focus on the interplay of cognition and language as captured by a network representation. To this end, we review different types of language network representations before considering the influence of global level network features. We continue by considering human performance in relation to network structure and conclude with theoretical network models that offer potential and testable explanations of cognitive and linguistic phenomena.

The Functional Neuroanatomy of Human Face Perception.

PubMed

Grill-Spector, Kalanit; Weiner, Kevin S; Kay, Kendrick; Gomez, Jesse

2017-09-15

Face perception is critical for normal social functioning and is mediated by a network of regions in the ventral visual stream. In this review, we describe recent neuroimaging findings regarding the macro- and microscopic anatomical features of the ventral face network, the characteristics of white matter connections, and basic computations performed by population receptive fields within face-selective regions composing this network. We emphasize the importance of the neural tissue properties and white matter connections of each region, as these anatomical properties may be tightly linked to the functional characteristics of the ventral face network. We end by considering how empirical investigations of the neural architecture of the face network may inform the development of computational models and shed light on how computations in the face network enable efficient face perception.

A fuzzy Bayesian network approach to quantify the human behaviour during an evacuation

NASA Astrophysics Data System (ADS)

Ramli, Nurulhuda; Ghani, Noraida Abdul; Ahmad, Nazihah

2016-06-01

Bayesian Network (BN) has been regarded as a successful representation of inter-relationship of factors affecting human behavior during an emergency. This paper is an extension of earlier work of quantifying the variables involved in the BN model of human behavior during an evacuation using a well-known direct probability elicitation technique. To overcome judgment bias and reduce the expert's burden in providing precise probability values, a new approach for the elicitation technique is required. This study proposes a new fuzzy BN approach for quantifying human behavior during an evacuation. Three major phases of methodology are involved, namely 1) development of qualitative model representing human factors during an evacuation, 2) quantification of BN model using fuzzy probability and 3) inferencing and interpreting the BN result. A case study of three inter-dependencies of human evacuation factors such as danger assessment ability, information about the threat and stressful conditions are used to illustrate the application of the proposed method. This approach will serve as an alternative to the conventional probability elicitation technique in understanding the human behavior during an evacuation.

Cytokines, chemokines, and colony-stimulating factors in human milk: the 1997 update.

PubMed

Garofalo, R P; Goldman, A S

1998-01-01

Epidemiologic studies conducted over the past 30 years to investigate the protective functions of human milk strongly support the notion that breast-feeding prevents infantile infections, particularly those affecting the gastrointestinal and respiratory tracts. However, more recent clinical and experimental observations also suggest that human milk not only provides passive protection, but also can directly modulate the immunological development of the recipient infant. The study of this remarkable defense system in human milk has been difficult due to its biochemical complexity, the small concentration of certain bioactive components, the compartmentalization of some of these agents, the dynamic quantitative and qualitative changes of milk during lactation, and the lack of specific reagents to quantify these agents. Nevertheless, a host of bioactive substances including hormones, growth factors, and immunological factors such as cytokines have been identified in human milk. Cytokines are pluripotent polypeptides that act in autocrine/paracrine fashions by binding to specific cellular receptors. They operate in networks and orchestrate the development and functions of the immune system. Several different cytokines and chemokines have been discovered in human milk over the past years, and the list is growing very rapidly. This article will review the current knowledge about the increasingly complex network of chemoattractants, activators, and anti-inflammatory cytokines present in human milk and their potential role in compensating for the developmental delay of the neonate immune system.

Cytokines in human milk.

PubMed

Garofalo, Roberto

2010-02-01

Epidemiologic studies conducted in the past 30 years to investigate the protective functions of human milk strongly support the notion that breastfeeding prevents infantile infections, particularly those affecting the gastrointestinal and respiratory tracts. However, more recent clinical and experimental observations also suggest that human milk not only provides passive protection, but also can directly modulate the immunological development of the recipient infant. The study of this remarkable defense system in human milk has been difficult because of its biochemical complexity, the small concentration of certain bioactive components, the compartmentalization of some of these agents, the dynamic quantitative and qualitative changes of milk during lactation, and the lack of specific reagents to quantify these agents. However, a host of bioactive substances, including hormones, growth factors, and immunological factors such as cytokines, have been identified in human milk. Cytokines are pluripotent polypeptides that act in autocrine/paracrine fashions by binding to specific cellular receptors. They operate in networks and orchestrate the development and functions of immune system. Several different cytokines and chemokines have been discovered in human milk in the past years, and the list is growing very rapidly. This article will review the current knowledge about the increasingly complex network of chemoattractants, activators, and anti-inflammatory cytokines present in human milk and their potential role in compensating for the developmental delay of the neonate immune system. Copyright 2010. Published by Mosby, Inc.

Extending network approach to language dynamics and human cognition. Comment on "Approaching human language with complex networks" by Cong and Liu

NASA Astrophysics Data System (ADS)

Gong, Tao; Shuai, Lan; Wu, Yicheng

2014-12-01

By analyzing complex networks constructed from authentic language data, Cong and Liu [1] advance linguistics research into the big data era. The network approach has revealed many intrinsic generalities and crucial differences at both the macro and micro scales between human languages. The axiom behind this research is that language is a complex adaptive system [2]. Although many lexical, semantic, or syntactic features have been discovered by means of analyzing the static and dynamic linguistic networks of world languages, available network-based language studies have not explicitly addressed the evolutionary dynamics of language systems and the correlations between language and human cognition. This commentary aims to provide some insights on how to use the network approach to study these issues.

Fruit development and ripening.

PubMed

Seymour, Graham B; Østergaard, Lars; Chapman, Natalie H; Knapp, Sandra; Martin, Cathie

2013-01-01

Fruiting structures in the angiosperms range from completely dry to highly fleshy organs and provide many of our major crop products, including grains. In the model plant Arabidopsis, which has dry fruits, a high-level regulatory network of transcription factors controlling fruit development has been revealed. Studies on rare nonripening mutations in tomato, a model for fleshy fruits, have provided new insights into the networks responsible for the control of ripening. It is apparent that there are strong similarities between dry and fleshy fruits in the molecular circuits governing development and maturation. Translation of information from tomato to other fleshy-fruited species indicates that regulatory networks are conserved across a wide spectrum of angiosperm fruit morphologies. Fruits are an essential part of the human diet, and recent developments in the sequencing of angiosperm genomes have provided the foundation for a step change in crop improvement through the understanding and harnessing of genome-wide genetic and epigenetic variation.

Large-scale integrative network-based analysis identifies common pathways disrupted by copy number alterations across cancers

PubMed Central

2013-01-01

Background Many large-scale studies analyzed high-throughput genomic data to identify altered pathways essential to the development and progression of specific types of cancer. However, no previous study has been extended to provide a comprehensive analysis of pathways disrupted by copy number alterations across different human cancers. Towards this goal, we propose a network-based method to integrate copy number alteration data with human protein-protein interaction networks and pathway databases to identify pathways that are commonly disrupted in many different types of cancer. Results We applied our approach to a data set of 2,172 cancer patients across 16 different types of cancers, and discovered a set of commonly disrupted pathways, which are likely essential for tumor formation in majority of the cancers. We also identified pathways that are only disrupted in specific cancer types, providing molecular markers for different human cancers. Analysis with independent microarray gene expression datasets confirms that the commonly disrupted pathways can be used to identify patient subgroups with significantly different survival outcomes. We also provide a network view of disrupted pathways to explain how copy number alterations affect pathways that regulate cell growth, cycle, and differentiation for tumorigenesis. Conclusions In this work, we demonstrated that the network-based integrative analysis can help to identify pathways disrupted by copy number alterations across 16 types of human cancers, which are not readily identifiable by conventional overrepresentation-based and other pathway-based methods. All the results and source code are available at http://compbio.cs.umn.edu/NetPathID/. PMID:23822816

World-wide satellite night-light data as a proxy of society-hydrology interaction and vulnerability to flood risk

NASA Astrophysics Data System (ADS)

Ceola, S.; Laio, F.; Montanari, A.

2013-12-01

The study and the analysis of the interactions and feedbacks between hydrology and society constitute the main issue of socio-hydrology. Recent flood events, which occurred across the globe, highlighted once again that mitigation strategies are needed to reduce flood risk. In particular, quick procedures for the identification of vulnerable human settlements and flood prone areas are a necessary tool to identify priorities for flood risk management. To this aim, a 19-year long period of world-wide night light data, as a proxy of human population, and the global river network have been examined. The spatio-temporal evolution of artificial luminosity depending on the distance from the river network has been assessed in order to quantitatively identify the likelihood for a populated pixel to be reached by water. The analysis focuses both on a global and on a local scale. Hotspots, such as highly illuminated areas and developing regions, have been also examined. The analysis shows an increment of yearly-averaged artificial luminosity from 1992 to 2010 (i.e. the time period of satellite data availability), whereas light intensity tends to decrease with increasing distance from the river network. The results thus reveal an increased vulnerability of human settlements to flooding events. A nearly 70-year long period of peace and the economic development after the Second World War could reasonably explain the observed enhancement of human population proximity to water bodies.

Development of the Intrinsic Language Network in Preschool Children from Ages 3 to 5 Years.

PubMed

Xiao, Yaqiong; Brauer, Jens; Lauckner, Mark; Zhai, Hongchang; Jia, Fucang; Margulies, Daniel S; Friederici, Angela D

2016-01-01

Resting state studies of spontaneous fluctuations in the functional magnetic resonance imaging (fMRI) blood oxygen level dependent signal have shown great potential in mapping the intrinsic functional connectivity of the human brain underlying cognitive functions. The aim of the present study was to explore the developmental changes in functional networks of the developing human brain exemplified with the language network in typically developing preschool children. To this end, resting-sate fMRI data were obtained from native Chinese children at ages of 3 and 5 years, 15 in each age group. Resting-state functional connectivity (RSFC) was analyzed for four regions of interest; these are the left and right anterior superior temporal gyrus (aSTG), left posterior superior temporal gyrus (pSTG), and left inferior frontal gyrus (IFG). The comparison of these RSFC maps between 3- and 5-year-olds revealed that RSFC decreases in the right aSTG and increases in the left hemisphere between aSTG seed and IFG, between pSTG seed and IFG, as well as between IFG seed and posterior superior temporal sulcus. In a subsequent analysis, functional asymmetry of the language network seeding in aSTG, pSTG and IFG was further investigated. The results showed an increase of left lateralization in both RSFC of pSTG and of IFG from ages 3 to 5 years. The IFG showed a leftward lateralized trend in 3-year-olds, while pSTG demonstrated rightward asymmetry in 5-year-olds. These findings suggest clear developmental trajectories of the language network between 3- and 5-year-olds revealed as a function of age, characterized by increasing long-range connections and dynamic hemispheric lateralization with age. Our study provides new insights into the developmental changes of a well-established functional network in young children and also offers a basis for future cross-culture and cross-age studies of the resting-state language network.

Development of global cortical networks in early infancy.

PubMed

Homae, Fumitaka; Watanabe, Hama; Otobe, Takayuki; Nakano, Tamami; Go, Tohshin; Konishi, Yukuo; Taga, Gentaro

2010-04-07

Human cognition and behaviors are subserved by global networks of neural mechanisms. Although the organization of the brain is a subject of interest, the process of development of global cortical networks in early infancy has not yet been clarified. In the present study, we explored developmental changes in these networks from several days to 6 months after birth by examining spontaneous fluctuations in brain activity, using multichannel near-infrared spectroscopy. We set up 94 measurement channels over the frontal, temporal, parietal, and occipital regions of the infant brain. The obtained signals showed complex time-series properties, which were characterized as 1/f fluctuations. To reveal the functional connectivity of the cortical networks, we calculated the temporal correlations of continuous signals between all the pairs of measurement channels. We found that the cortical network organization showed regional dependency and dynamic changes in the course of development. In the temporal, parietal, and occipital regions, connectivity increased between homologous regions in the two hemispheres and within hemispheres; in the frontal regions, it decreased progressively. Frontoposterior connectivity changed to a "U-shaped" pattern within 6 months: it decreases from the neonatal period to the age of 3 months and increases from the age of 3 months to the age of 6 months. We applied cluster analyses to the correlation coefficients and showed that the bilateral organization of the networks begins to emerge during the first 3 months of life. Our findings suggest that these developing networks, which form multiple clusters, are precursors of the functional cerebral architecture.

The Sub-Regional Functional Organization of Neocortical Irritative Epileptic Networks in Pediatric Epilepsy

PubMed Central

Janca, Radek; Krsek, Pavel; Jezdik, Petr; Cmejla, Roman; Tomasek, Martin; Komarek, Vladimir; Marusic, Petr; Jiruska, Premysl

2018-01-01

Between seizures, irritative network generates frequent brief synchronous activity, which manifests on the EEG as interictal epileptiform discharges (IEDs). Recent insights into the mechanism of IEDs at the microscopic level have demonstrated a high variance in the recruitment of neuronal populations generating IEDs and a high variability in the trajectories through which IEDs propagate across the brain. These phenomena represent one of the major constraints for precise characterization of network organization and for the utilization of IEDs during presurgical evaluations. We have developed a new approach to dissect human neocortical irritative networks and quantify their properties. We have demonstrated that irritative network has modular nature and it is composed of multiple independent sub-regions, each with specific IED propagation trajectories and differing in the extent of IED activity generated. The global activity of the irritative network is determined by long-term and circadian fluctuations in sub-region spatiotemporal properties. Also, the most active sub-region co-localizes with the seizure onset zone in 12/14 cases. This study demonstrates that principles of recruitment variability and propagation are conserved at the macroscopic level and that they determine irritative network properties in humans. Functional stratification of the irritative network increases the diagnostic yield of intracranial investigations with the potential to improve the outcomes of surgical treatment of neocortical epilepsy. PMID:29628910

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

PubMed

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

Model development of a participatory Bayesian network for coupling ecosystem services into integrated water resources management

NASA Astrophysics Data System (ADS)

Xue, Jie; Gui, Dongwei; Lei, Jiaqiang; Zeng, Fanjiang; Mao, Donglei; Zhang, Zhiwei

2017-11-01

There is an increasing consensus on the importance of coupling ecosystem services (ES) into integrated water resource management (IWRM), due to a wide range of benefits to human from the ES. This paper proposes an ES-based IWRM framework within which a participatory Bayesian network (BN) model is developed to assist with the coupling between ES and IWRM. The framework includes three steps: identifying water-related services of ecosystems; analysis of the tradeoff and synergy among users of water; and ES-based IWRM implementation using the participatory BN model. We present the development, evaluation and application of the participatory BN model with the involvement of four participant groups (stakeholders, water manager, water management experts, and research team) in Qira oasis area, Northwest China. As a typical catchment-scale region, the Qira oasis area is facing severe water competition between the demands of human activities and natural ecosystems. Results demonstrate that the BN model developed provides effective integration of ES into a quantitative IWMR framework via public negotiation and feedback. The network results, sensitivity evaluation, and management scenarios are broadly accepted by the participant groups. The intervention scenarios from the model conclude that any water management measure remains unable to sustain the ecosystem health in water-related ES. Greater cooperation among the stakeholders is highly necessary for dealing with such water conflicts. In particular, a proportion of the agricultural water saved through improving water-use efficiency should be transferred to natural ecosystems via water trade. The BN model developed is appropriate for areas throughout the world in which there is intense competition for water between human activities and ecosystems.

78 FR 69857 - Eunice Kennedy Shriver National Institute of Child Health and Human Development; Notice of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-21

... research opportunities and needs; Renewing research infrastructure network program. Place: Hyatt Regency...., Acting Director, National Center for Medical Rehabilitation Research (NCMRR), Director, Biological...

Systems biology in hepatology: approaches and applications.

PubMed

Mardinoglu, Adil; Boren, Jan; Smith, Ulf; Uhlen, Mathias; Nielsen, Jens

2018-06-01

Detailed insights into the biological functions of the liver and an understanding of its crosstalk with other human tissues and the gut microbiota can be used to develop novel strategies for the prevention and treatment of liver-associated diseases, including fatty liver disease, cirrhosis, hepatocellular carcinoma and type 2 diabetes mellitus. Biological network models, including metabolic, transcriptional regulatory, protein-protein interaction, signalling and co-expression networks, can provide a scaffold for studying the biological pathways operating in the liver in connection with disease development in a systematic manner. Here, we review studies in which biological network models were used to integrate multiomics data to advance our understanding of the pathophysiological responses of complex liver diseases. We also discuss how this mechanistic approach can contribute to the discovery of potential biomarkers and novel drug targets, which might lead to the design of targeted and improved treatment strategies. Finally, we present a roadmap for the successful integration of models of the liver and other human tissues with the gut microbiota to simulate whole-body metabolic functions in health and disease.

The Emerging Wireless Body Area Network on Android Smartphones: A Review

NASA Astrophysics Data System (ADS)

Puspitaningayu, P.; Widodo, A.; Yundra, E.

2018-01-01

Our society now has driven us into an era where almost everything can be digitally monitored and controlled including the human body. The growth of wireless body area network (WBAN), as a specific scope of sensor networks which mounted or attached to human body also developing rapidly. It allows people to monitor their health and several daily activities. This study is intended to review the trend of WBAN especially on Android, one of the most popular smartphone platforms. A systematic literature review is concerned to the following parameters: the purpose of the device and/or application, the type of sensors, the type of Android device, and its connectivity. Most of the studies were more concern to healthcare or medical monitoring systems: blood pressure, electro cardiograph, tremor detection, etc. On the other hand, the rest of them aimed for activity tracker, environment sensing, and epidemic control. After all, those studies shown that not only Android can be a powerful platform to process data from various sensors but also smartphones can be a good alternative to develop WBANs for medical and other daily applications.

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants.

PubMed

Cruz-Garza, Jesus G; Hernandez, Zachery R; Tse, Teresa; Caducoy, Eunice; Abibullaev, Berdakh; Contreras-Vidal, Jose L

2015-10-04

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing "in action and in context". This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.

Attentional Bias in Human Category Learning: The Case of Deep Learning.

PubMed

Hanson, Catherine; Caglar, Leyla Roskan; Hanson, Stephen José

2018-01-01

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This "failure" to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars) are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning.

Network Physiology: How Organ Systems Dynamically Interact

PubMed Central

Bartsch, Ronny P.; Liu, Kang K. L.; Bashan, Amir; Ivanov, Plamen Ch.

2015-01-01

We systematically study how diverse physiologic systems in the human organism dynamically interact and collectively behave to produce distinct physiologic states and functions. This is a fundamental question in the new interdisciplinary field of Network Physiology, and has not been previously explored. Introducing the novel concept of Time Delay Stability (TDS), we develop a computational approach to identify and quantify networks of physiologic interactions from long-term continuous, multi-channel physiological recordings. We also develop a physiologically-motivated visualization framework to map networks of dynamical organ interactions to graphical objects encoded with information about the coupling strength of network links quantified using the TDS measure. Applying a system-wide integrative approach, we identify distinct patterns in the network structure of organ interactions, as well as the frequency bands through which these interactions are mediated. We establish first maps representing physiologic organ network interactions and discover basic rules underlying the complex hierarchical reorganization in physiologic networks with transitions across physiologic states. Our findings demonstrate a direct association between network topology and physiologic function, and provide new insights into understanding how health and distinct physiologic states emerge from networked interactions among nonlinear multi-component complex systems. The presented here investigations are initial steps in building a first atlas of dynamic interactions among organ systems. PMID:26555073

Human brain networks function in connectome-specific harmonic waves.

PubMed

Atasoy, Selen; Donnelly, Isaac; Pearson, Joel

2016-01-21

A key characteristic of human brain activity is coherent, spatially distributed oscillations forming behaviour-dependent brain networks. However, a fundamental principle underlying these networks remains unknown. Here we report that functional networks of the human brain are predicted by harmonic patterns, ubiquitous throughout nature, steered by the anatomy of the human cerebral cortex, the human connectome. We introduce a new technique extending the Fourier basis to the human connectome. In this new frequency-specific representation of cortical activity, that we call 'connectome harmonics', oscillatory networks of the human brain at rest match harmonic wave patterns of certain frequencies. We demonstrate a neural mechanism behind the self-organization of connectome harmonics with a continuous neural field model of excitatory-inhibitory interactions on the connectome. Remarkably, the critical relation between the neural field patterns and the delicate excitation-inhibition balance fits the neurophysiological changes observed during the loss and recovery of consciousness.

Human Environmental Disease Network: A computational model to assess toxicology of contaminants.

PubMed

Taboureau, Olivier; Audouze, Karine

2017-01-01

During the past decades, many epidemiological, toxicological and biological studies have been performed to assess the role of environmental chemicals as potential toxicants associated with diverse human disorders. However, the relationships between diseases based on chemical exposure rarely have been studied by computational biology. We developed a human environmental disease network (EDN) to explore and suggest novel disease-disease and chemical-disease relationships. The presented scored EDN model is built upon the integration of systems biology and chemical toxicology using information on chemical contaminants and their disease relationships reported in the TDDB database. The resulting human EDN takes into consideration the level of evidence of the toxicant-disease relationships, allowing inclusion of some degrees of significance in the disease-disease associations. Such a network can be used to identify uncharacterized connections between diseases. Examples are discussed for type 2 diabetes (T2D). Additionally, this computational model allows confirmation of already known links between chemicals and diseases (e.g., between bisphenol A and behavioral disorders) and also reveals unexpected associations between chemicals and diseases (e.g., between chlordane and olfactory alteration), thus predicting which chemicals may be risk factors to human health. The proposed human EDN model allows exploration of common biological mechanisms of diseases associated with chemical exposure, helping us to gain insight into disease etiology and comorbidity. This computational approach is an alternative to animal testing supporting the 3R concept.

A systematic atlas of chaperome deregulation topologies across the human cancer landscape

PubMed Central

Sverchkova, Angelina

2018-01-01

Proteome balance is safeguarded by the proteostasis network (PN), an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Since the chaperome is among the central components of the PN, we focused on the chaperome in our study by utilizing a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analyses highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro2) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of chaperome shifts in human cancers and sets the stage for a systematic global analysis of PN alterations across the human diseasome towards novel hypotheses for therapeutic network re-adjustment in proteostasis disorders. PMID:29293508

A systematic atlas of chaperome deregulation topologies across the human cancer landscape.

PubMed

Hadizadeh Esfahani, Ali; Sverchkova, Angelina; Saez-Rodriguez, Julio; Schuppert, Andreas A; Brehme, Marc

2018-01-01

Proteome balance is safeguarded by the proteostasis network (PN), an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Since the chaperome is among the central components of the PN, we focused on the chaperome in our study by utilizing a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analyses highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro2) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of chaperome shifts in human cancers and sets the stage for a systematic global analysis of PN alterations across the human diseasome towards novel hypotheses for therapeutic network re-adjustment in proteostasis disorders.

Symbolic modeling of human anatomy for visualization and simulation

NASA Astrophysics Data System (ADS)

Pommert, Andreas; Schubert, Rainer; Riemer, Martin; Schiemann, Thomas; Tiede, Ulf; Hoehne, Karl H.

1994-09-01

Visualization of human anatomy in a 3D atlas requires both spatial and more abstract symbolic knowledge. Within our 'intelligent volume' model which integrates these two levels, we developed and implemented a semantic network model for describing human anatomy. Concepts for structuring (abstraction levels, domains, views, generic and case-specific modeling, inheritance) are introduced. Model, tools for generation and exploration and applications in our 3D anatomical atlas are presented and discussed.

Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome

PubMed Central

Hannigan, Geoffrey D.; Duhaime, Melissa B.; Koutra, Danai

2018-01-01

Viruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks. PMID:29668682

Biogeography and environmental conditions shape bacteriophage-bacteria networks across the human microbiome.

PubMed

Hannigan, Geoffrey D; Duhaime, Melissa B; Koutra, Danai; Schloss, Patrick D

2018-04-01

Viruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks.

Scaling identity connects human mobility and social interactions.

PubMed

Deville, Pierre; Song, Chaoming; Eagle, Nathan; Blondel, Vincent D; Barabási, Albert-László; Wang, Dashun

2016-06-28

Massive datasets that capture human movements and social interactions have catalyzed rapid advances in our quantitative understanding of human behavior during the past years. One important aspect affecting both areas is the critical role space plays. Indeed, growing evidence suggests both our movements and communication patterns are associated with spatial costs that follow reproducible scaling laws, each characterized by its specific critical exponents. Although human mobility and social networks develop concomitantly as two prolific yet largely separated fields, we lack any known relationships between the critical exponents explored by them, despite the fact that they often study the same datasets. Here, by exploiting three different mobile phone datasets that capture simultaneously these two aspects, we discovered a new scaling relationship, mediated by a universal flux distribution, which links the critical exponents characterizing the spatial dependencies in human mobility and social networks. Therefore, the widely studied scaling laws uncovered in these two areas are not independent but connected through a deeper underlying reality.

Modeling Physiological Systems in the Human Body as Networks of Quasi-1D Fluid Flows

NASA Astrophysics Data System (ADS)

Staples, Anne

2008-11-01

Extensive research has been done on modeling human physiology. Most of this work has been aimed at developing detailed, three-dimensional models of specific components of physiological systems, such as a cell, a vein, a molecule, or a heart valve. While efforts such as these are invaluable to our understanding of human biology, if we were to construct a global model of human physiology with this level of detail, computing even a nanosecond in this computational being's life would certainly be prohibitively expensive. With this in mind, we derive the Pulsed Flow Equations, a set of coupled one-dimensional partial differential equations, specifically designed to capture two-dimensional viscous, transport, and other effects, and aimed at providing accurate and fast-to-compute global models for physiological systems represented as networks of quasi one-dimensional fluid flows. Our goal is to be able to perform faster-than-real time simulations of global processes in the human body on desktop computers.

Scaling identity connects human mobility and social interactions

PubMed Central

Deville, Pierre; Song, Chaoming; Eagle, Nathan; Blondel, Vincent D.; Barabási, Albert-László; Wang, Dashun

2016-01-01

Massive datasets that capture human movements and social interactions have catalyzed rapid advances in our quantitative understanding of human behavior during the past years. One important aspect affecting both areas is the critical role space plays. Indeed, growing evidence suggests both our movements and communication patterns are associated with spatial costs that follow reproducible scaling laws, each characterized by its specific critical exponents. Although human mobility and social networks develop concomitantly as two prolific yet largely separated fields, we lack any known relationships between the critical exponents explored by them, despite the fact that they often study the same datasets. Here, by exploiting three different mobile phone datasets that capture simultaneously these two aspects, we discovered a new scaling relationship, mediated by a universal flux distribution, which links the critical exponents characterizing the spatial dependencies in human mobility and social networks. Therefore, the widely studied scaling laws uncovered in these two areas are not independent but connected through a deeper underlying reality. PMID:27274050

Space Network Control Conference on Resource Allocation Concepts and Approaches

NASA Technical Reports Server (NTRS)

Moe, Karen L. (Editor)

1991-01-01

The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process.

Hybrid routing technique for a fault-tolerant, integrated information network

NASA Technical Reports Server (NTRS)

Meredith, B. D.

1986-01-01

The evolutionary growth of the space station and the diverse activities onboard are expected to require a hierarchy of integrated, local area networks capable of supporting data, voice, and video communications. In addition, fault-tolerant network operation is necessary to protect communications between critical systems attached to the net and to relieve the valuable human resources onboard the space station of time-critical data system repair tasks. A key issue for the design of the fault-tolerant, integrated network is the development of a robust routing algorithm which dynamically selects the optimum communication paths through the net. A routing technique is described that adapts to topological changes in the network to support fault-tolerant operation and system evolvability.

Resilience of natural gas networks during conflicts, crises and disruptions.

PubMed

Carvalho, Rui; Buzna, Lubos; Bono, Flavio; Masera, Marcelo; Arrowsmith, David K; Helbing, Dirk

2014-01-01

Human conflict, geopolitical crises, terrorist attacks, and natural disasters can turn large parts of energy distribution networks offline. Europe's current gas supply network is largely dependent on deliveries from Russia and North Africa, creating vulnerabilities to social and political instabilities. During crises, less delivery may mean greater congestion, as the pipeline network is used in ways it has not been designed for. Given the importance of the security of natural gas supply, we develop a model to handle network congestion on various geographical scales. We offer a resilient response strategy to energy shortages and quantify its effectiveness for a variety of relevant scenarios. In essence, Europe's gas supply can be made robust even to major supply disruptions, if a fair distribution strategy is applied.

Human Fetal Brain Connectome: Structural Network Development from Middle Fetal Stage to Birth

PubMed Central

Song, Limei; Mishra, Virendra; Ouyang, Minhui; Peng, Qinmu; Slinger, Michelle; Liu, Shuwei; Huang, Hao

2017-01-01

Complicated molecular and cellular processes take place in a spatiotemporally heterogeneous and precisely regulated pattern in the human fetal brain, yielding not only dramatic morphological and microstructural changes, but also macroscale connectomic transitions. As the underlying substrate of the fetal brain structural network, both dynamic neuronal migration pathways and rapid developing fetal white matter (WM) fibers could fundamentally reshape early fetal brain connectome. Quantifying structural connectome development can not only shed light on the brain reconfiguration in this critical yet rarely studied developmental period, but also reveal alterations of the connectome under neuropathological conditions. However, transition of the structural connectome from the mid-fetal stage to birth is not yet known. The contribution of different types of neural fibers to the structural network in the mid-fetal brain is not known, either. In this study, diffusion tensor magnetic resonance imaging (DT-MRI or DTI) of 10 fetal brain specimens at the age of 20 postmenstrual weeks (PMW), 12 in vivo brains at 35 PMW, and 12 in vivo brains at term (40 PMW) were acquired. The structural connectome of each brain was established with evenly parcellated cortical regions as network nodes and traced fiber pathways based on DTI tractography as network edges. Two groups of fibers were categorized based on the fiber terminal locations in the cerebral wall in the 20 PMW fetal brains. We found that fetal brain networks become stronger and more efficient during 20–40 PMW. Furthermore, network strength and global efficiency increase more rapidly during 20–35 PMW than during 35–40 PMW. Visualization of the whole brain fiber distribution by the lengths suggested that the network reconfiguration in this developmental period could be associated with a significant increase of major long association WM fibers. In addition, non-WM neural fibers could be a major contributor to the structural network configuration at 20 PMW and small-world network organization could exist as early as 20 PMW. These findings offer a preliminary record of the fetal brain structural connectome maturation from the middle fetal stage to birth and reveal the critical role of non-WM neural fibers in structural network configuration in the middle fetal stage. PMID:29081731

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

PubMed Central

2017-01-01

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson's disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about 0.729 ± 0.16 for decoding movement from the resting state and about 0.671 ± 0.14 for decoding left and right visually cued movements. PMID:29201041

Optogenetic stimulation of multiwell MEA plates for neural and cardiac applications

NASA Astrophysics Data System (ADS)

Clements, Isaac P.; Millard, Daniel C.; Nicolini, Anthony M.; Preyer, Amanda J.; Grier, Robert; Heckerling, Andrew; Blum, Richard A.; Tyler, Phillip; McSweeney, K. M.; Lu, Yi-Fan; Hall, Diana; Ross, James D.

2016-03-01

Microelectrode array (MEA) technology enables advanced drug screening and "disease-in-a-dish" modeling by measuring the electrical activity of cultured networks of neural or cardiac cells. Recent developments in human stem cell technologies, advancements in genetic models, and regulatory initiatives for drug screening have increased the demand for MEA-based assays. In response, Axion Biosystems previously developed a multiwell MEA platform, providing up to 96 MEA culture wells arrayed into a standard microplate format. Multiwell MEA-based assays would be further enhanced by optogenetic stimulation, which enables selective excitation and inhibition of targeted cell types. This capability for selective control over cell culture states would allow finer pacing and probing of cell networks for more reliable and complete characterization of complex network dynamics. Here we describe a system for independent optogenetic stimulation of each well of a 48-well MEA plate. The system enables finely graded control of light delivery during simultaneous recording of network activity in each well. Using human induced pluripotent stem cell (hiPSC) derived cardiomyocytes and rodent primary neuronal cultures, we demonstrate high channel-count light-based excitation and suppression in several proof-of-concept experimental models. Our findings demonstrate advantages of combining multiwell optical stimulation and MEA recording for applications including cardiac safety screening, neural toxicity assessment, and advanced characterization of complex neuronal diseases.

The offer network protocol: Mathematical foundations and a roadmap for the development of a global brain

NASA Astrophysics Data System (ADS)

Heylighen, Francis

2017-01-01

The world is confronted with a variety of interdependent problems, including scarcity, unsustainability, inequality, pollution and poor governance. Tackling such complex challenges requires coordinated action. The present paper proposes the development of a self-organizing system for coordination, called an "offer network", that would use the distributed intelligence of the Internet to match the offers and needs of all human, technological and natural agents on the planet. This would maximize synergy and thus minimize waste and scarcity of resources. Implementing such coordination requires a protocol that formally defines agents, offers, needs, and the network of condition-action rules or reactions that interconnect them. Matching algorithms can then determine self-sustaining subnetworks in which each consumed resource (need) is also produced (offer). After sketching the elements of a mathematical foundation for offer networks, the paper proposes a roadmap for their practical implementation. This includes step-by-step integration with technologies such as the Semantic Web, ontologies, the Internet of Things, reputation and recommendation systems, reinforcement learning, governance through legal constraints and nudging, and ecosystem modeling. The resulting intelligent platform should be able to tackle nearly all practical and theoretical problems in a bottom-up, distributed manner, thus functioning like a Global Brain for humanity.

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Building the team for team science

USGS Publications Warehouse

Read, Emily K.; O'Rourke, M.; Hong, G. S.; Hanson, P. C.; Winslow, Luke A.; Crowley, S.; Brewer, C. A.; Weathers, K. C.

2016-01-01

The ability to effectively exchange information and develop trusting, collaborative relationships across disciplinary boundaries is essential for 21st century scientists charged with solving complex and large-scale societal and environmental challenges, yet these communication skills are rarely taught. Here, we describe an adaptable training program designed to increase the capacity of scientists to engage in information exchange and relationship development in team science settings. A pilot of the program, developed by a leader in ecological network science, the Global Lake Ecological Observatory Network (GLEON), indicates that the training program resulted in improvement in early career scientists’ confidence in team-based network science collaborations within and outside of the program. Fellows in the program navigated human-network challenges, expanded communication skills, and improved their ability to build professional relationships, all in the context of producing collaborative scientific outcomes. Here, we describe the rationale for key communication training elements and provide evidence that such training is effective in building essential team science skills.

Sustaining Research Networks: the Twenty-Year Experience of the HMO Research Network

PubMed Central

Steiner, John F.; Paolino, Andrea R.; Thompson, Ella E.; Larson, Eric B.

2014-01-01

Purpose: As multi-institutional research networks assume a central role in clinical research, they must address the challenge of sustainability. Despite its importance, the concept of network sustainability has received little attention in the literature, and the sustainability strategies of durable scientific networks have not been described. Innovation: The Health Maintenance Organization Research Network (HMORN) is a consortium of 18 research departments in integrated health care delivery systems with over 15 million members in the United States and Israel. The HMORN has coordinated federally funded scientific networks and studies since 1994. This case study describes the HMORN approach to sustainability, proposes an operational definition of network sustainability, and identifies 10 essential elements that can enhance sustainability. Credibility: The sustainability framework proposed here is drawn from prior publications on organizational issues by HMORN investigators and from the experience of recent HMORN leaders and senior staff. Conclusion and Discussion: Network sustainability can be defined as (1) the development and enhancement of shared research assets to facilitate a sequence of research studies in a specific content area or multiple areas, and (2) a community of researchers and other stakeholders who reuse and develop those assets. Essential elements needed to develop the shared assets of a network include: network governance; trustworthy data and processes for sharing data; shared knowledge about research tools; administrative efficiency; physical infrastructure; and infrastructure funding. The community of researchers within a network is enhanced by: a clearly defined mission, vision and values; protection of human subjects; a culture of collaboration; and strong relationships with host organizations. While the importance of these elements varies based on the membership and goals of a network, this framework for sustainability can enhance strategic planning within the network and can guide relationships with external stakeholders. PMID:25848605

A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy.

PubMed

Kell, Alexander J E; Yamins, Daniel L K; Shook, Erica N; Norman-Haignere, Sam V; McDermott, Josh H

2018-05-02

A core goal of auditory neuroscience is to build quantitative models that predict cortical responses to natural sounds. Reasoning that a complete model of auditory cortex must solve ecologically relevant tasks, we optimized hierarchical neural networks for speech and music recognition. The best-performing network contained separate music and speech pathways following early shared processing, potentially replicating human cortical organization. The network performed both tasks as well as humans and exhibited human-like errors despite not being optimized to do so, suggesting common constraints on network and human performance. The network predicted fMRI voxel responses substantially better than traditional spectrotemporal filter models throughout auditory cortex. It also provided a quantitative signature of cortical representational hierarchy-primary and non-primary responses were best predicted by intermediate and late network layers, respectively. The results suggest that task optimization provides a powerful set of tools for modeling sensory systems. Copyright © 2018 Elsevier Inc. All rights reserved.

Security and Privacy Preservation in Human-Involved Networks

NASA Astrophysics Data System (ADS)

Asher, Craig; Aumasson, Jean-Philippe; Phan, Raphael C.-W.

This paper discusses security within human-involved networks, with a focus on social networking services (SNS). We argue that more secure networks could be designed using semi-formal security models inspired from cryptography, as well as notions like that of ceremony, which exploits human-specific abilities and psychology to assist creating more secure protocols. We illustrate some of our ideas with the example of the SNS Facebook.

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

PubMed

Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-03-22

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.

A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

PubMed Central

Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-01-01

Background Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. Methodology We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. Conclusions This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking. PMID:21445339

Power to the Pelvis: Strengthening Your Pelvic Floor Muscles

MedlinePlus

... to Anyone Battling a Bulging Hernia Keeping Your Gut in Check The Power of Your Pancreas Wise ... Kennedy Shriver National Institute of Child Health and Human Development Pelvic Floor Disorders Network. Am J Obstet ...

VETNET ECER 2002 Proceedings: Current Research in European Vocational Education and Human Resource Development. Proceedings of the Programme Presented by the Research Network on Vocational Education and Training (VETNET) at the European Conference of Educational Research (ECER) (5th, Lisbon, Portugual, September 11-14, 2002).

ERIC Educational Resources Information Center

Manning, Sabine, Ed.; Griffiths, Toni, Ed.; Oliveira, Teresa, Ed.

This document contains the papers from a conference on current research in vocational education and training (VET) and human resource development in Europe. The following papers are among those included: "The Contribution of the German Pilot Project 'New Learning Concepts within the Dual Vocational Education and Training System' towards the…

Functional roles of fibroblast growth factor receptors (FGFRs) signaling in human cancers.

PubMed

Tiong, Kai Hung; Mah, Li Yen; Leong, Chee-Onn

2013-12-01

The fibroblast growth factor receptors (FGFRs) regulate important biological processes including cell proliferation and differentiation during development and tissue repair. Over the past decades, numerous pathological conditions and developmental syndromes have emerged as a consequence of deregulation in the FGFRs signaling network. This review aims to provide an overview of FGFR family, their complex signaling pathways in tumorigenesis, and the current development and application of therapeutics targeting the FGFRs signaling for treatment of refractory human cancers.

Control of fluxes in metabolic networks

PubMed Central

Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim; Barabási, Albert-László; Liu, Yang-Yu

2016-01-01

Understanding the control of large-scale metabolic networks is central to biology and medicine. However, existing approaches either require specifying a cellular objective or can only be used for small networks. We introduce new coupling types describing the relations between reaction activities, and develop an efficient computational framework, which does not require any cellular objective for systematic studies of large-scale metabolism. We identify the driver reactions facilitating control of 23 metabolic networks from all kingdoms of life. We find that unicellular organisms require a smaller degree of control than multicellular organisms. Driver reactions are under complex cellular regulation in Escherichia coli, indicating their preeminent role in facilitating cellular control. In human cancer cells, driver reactions play pivotal roles in malignancy and represent potential therapeutic targets. The developed framework helps us gain insights into regulatory principles of diseases and facilitates design of engineering strategies at the interface of gene regulation, signaling, and metabolism. PMID:27197218

[The Development of Information Centralization and Management Integration System for Monitors Based on Wireless Sensor Network].

PubMed

Xu, Xiu; Zhang, Honglei; Li, Yiming; Li, Bin

2015-07-01

Developed the information centralization and management integration system for monitors of different brands and models with wireless sensor network technologies such as wireless location and wireless communication, based on the existing wireless network. With adaptive implementation and low cost, the system which possesses the advantages of real-time, efficiency and elaboration is able to collect status and data of the monitors, locate the monitors, and provide services with web server, video server and locating server via local network. Using an intranet computer, the clinical and device management staffs can access the status and parameters of monitors. Applications of this system provide convenience and save human resource for clinical departments, as well as promote the efficiency, accuracy and elaboration for the device management. The successful achievement of this system provides solution for integrated and elaborated management of the mobile devices including ventilator and infusion pump.

Mapping and Quantification of Vascular Branching in Plants, Animals and Humans by VESGEN Software

NASA Technical Reports Server (NTRS)

Parsons-Wingerter, Patricia A.; Vickerman, Mary B.; Keith, Patricia A.

2010-01-01

Humans face daunting challenges in the successful exploration and colonization of space, including adverse alterations in gravity and radiation. The Earth-determined biology of humans, animals and plants is significantly modified in such extraterrestrial environments. One physiological requirement shared by humans with larger plants and animals is a complex, highly branching vascular system that is dynamically responsive to cellular metabolism, immunological protection and specialized cellular/tissue function. The VESsel GENeration (VESGEN) Analysis has been developed as a mature beta version, pre-release research software for mapping and quantification of the fractal-based complexity of vascular branching. Alterations in vascular branching pattern can provide informative read-outs of altered vascular regulation. Originally developed for biomedical applications in angiogenesis, VESGEN 2D has provided novel insights into the cytokine, transgenic and therapeutic regulation of angiogenesis, lymphangiogenesis and other microvascular remodeling phenomena. Vascular trees, networks and tree-network composites are mapped and quantified. Applications include disease progression from clinical ophthalmic images of the human retina; experimental regulation of vascular remodeling in the mouse retina; avian and mouse coronary vasculature, and other experimental models in vivo. We envision that altered branching in the leaves of plants studied on ISS such as Arabidopsis thaliana cans also be analyzed.

Mapping and Quantification of Vascular Branching in Plants, Animals and Humans by VESGEN Software

NASA Technical Reports Server (NTRS)

Parsons-Wingerter, P. A.; Vickerman, M. B.; Keith, P. A.

2010-01-01

Humans face daunting challenges in the successful exploration and colonization of space, including adverse alterations in gravity and radiation. The Earth-determined biology of plants, animals and humans is significantly modified in such extraterrestrial environments. One physiological requirement shared by larger plants and animals with humans is a complex, highly branching vascular system that is dynamically responsive to cellular metabolism, immunological protection and specialized cellular/tissue function. VESsel GENeration (VESGEN) Analysis has been developed as a mature beta version, pre-release research software for mapping and quantification of the fractal-based complexity of vascular branching. Alterations in vascular branching pattern can provide informative read-outs of altered vascular regulation. Originally developed for biomedical applications in angiogenesis, VESGEN 2D has provided novel insights into the cytokine, transgenic and therapeutic regulation of angiogenesis, lymphangiogenesis and other microvascular remodeling phenomena. Vascular trees, networks and tree-network composites are mapped and quantified. Applications include disease progression from clinical ophthalmic images of the human retina; experimental regulation of vascular remodeling in the mouse retina; avian and mouse coronary vasculature, and other experimental models in vivo. We envision that altered branching in the leaves of plants studied on ISS such as Arabidopsis thaliana cans also be analyzed.

H3ABioNet, a sustainable pan-African bioinformatics network for human heredity and health in Africa

PubMed Central

Mulder, Nicola J.; Adebiyi, Ezekiel; Alami, Raouf; Benkahla, Alia; Brandful, James; Doumbia, Seydou; Everett, Dean; Fadlelmola, Faisal M.; Gaboun, Fatima; Gaseitsiwe, Simani; Ghazal, Hassan; Hazelhurst, Scott; Hide, Winston; Ibrahimi, Azeddine; Jaufeerally Fakim, Yasmina; Jongeneel, C. Victor; Joubert, Fourie; Kassim, Samar; Kayondo, Jonathan; Kumuthini, Judit; Lyantagaye, Sylvester; Makani, Julie; Mansour Alzohairy, Ahmed; Masiga, Daniel; Moussa, Ahmed; Nash, Oyekanmi; Ouwe Missi Oukem-Boyer, Odile; Owusu-Dabo, Ellis; Panji, Sumir; Patterton, Hugh; Radouani, Fouzia; Sadki, Khalid; Seghrouchni, Fouad; Tastan Bishop, Özlem; Tiffin, Nicki; Ulenga, Nzovu

2016-01-01

The application of genomics technologies to medicine and biomedical research is increasing in popularity, made possible by new high-throughput genotyping and sequencing technologies and improved data analysis capabilities. Some of the greatest genetic diversity among humans, animals, plants, and microbiota occurs in Africa, yet genomic research outputs from the continent are limited. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive the development of genomic research for human health in Africa, and through recognition of the critical role of bioinformatics in this process, spurred the establishment of H3ABioNet, a pan-African bioinformatics network for H3Africa. The limitations in bioinformatics capacity on the continent have been a major contributory factor to the lack of notable outputs in high-throughput biology research. Although pockets of high-quality bioinformatics teams have existed previously, the majority of research institutions lack experienced faculty who can train and supervise bioinformatics students. H3ABioNet aims to address this dire need, specifically in the area of human genetics and genomics, but knock-on effects are ensuring this extends to other areas of bioinformatics. Here, we describe the emergence of genomics research and the development of bioinformatics in Africa through H3ABioNet. PMID:26627985

Stochastic cycle selection in active flow networks.

PubMed

Woodhouse, Francis G; Forrow, Aden; Fawcett, Joanna B; Dunkel, Jörn

2016-07-19

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.

Stochastic cycle selection in active flow networks

PubMed Central

Woodhouse, Francis G.; Forrow, Aden; Fawcett, Joanna B.; Dunkel, Jörn

2016-01-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models. PMID:27382186

Space Communications and Navigation (SCaN) Network Simulation Tool Development and Its Use Cases

NASA Technical Reports Server (NTRS)

Jennings, Esther; Borgen, Richard; Nguyen, Sam; Segui, John; Stoenescu, Tudor; Wang, Shin-Ywan; Woo, Simon; Barritt, Brian; Chevalier, Christine; Eddy, Wesley

2009-01-01

In this work, we focus on the development of a simulation tool to assist in analysis of current and future (proposed) network architectures for NASA. Specifically, the Space Communications and Navigation (SCaN) Network is being architected as an integrated set of new assets and a federation of upgraded legacy systems. The SCaN architecture for the initial missions for returning humans to the moon and beyond will include the Space Network (SN) and the Near-Earth Network (NEN). In addition to SCaN, the initial mission scenario involves a Crew Exploration Vehicle (CEV), the International Space Station (ISS) and NASA Integrated Services Network (NISN). We call the tool being developed the SCaN Network Integration and Engineering (SCaN NI&E) Simulator. The intended uses of such a simulator are: (1) to characterize performance of particular protocols and configurations in mission planning phases; (2) to optimize system configurations by testing a larger parameter space than may be feasible in either production networks or an emulated environment; (3) to test solutions in order to find issues/risks before committing more significant resources needed to produce real hardware or flight software systems. We describe two use cases of the tool: (1) standalone simulation of CEV to ISS baseline scenario to determine network performance, (2) participation in Distributed Simulation Integration Laboratory (DSIL) tests to perform function testing and verify interface and interoperability of geographically dispersed simulations/emulations.

Platelet lysate gel and endothelial progenitors stimulate microvascular network formation in vitro: tissue engineering implications.

PubMed

Fortunato, Tiago M; Beltrami, Cristina; Emanueli, Costanza; De Bank, Paul A; Pula, Giordano

2016-05-04

Revascularisation is a key step for tissue regeneration and complete organ engineering. We describe the generation of human platelet lysate gel (hPLG), an extracellular matrix preparation from human platelets able to support the proliferation of endothelial colony forming cells (ECFCs) in 2D cultures and the formation of a complete microvascular network in vitro in 3D cultures. Existing extracellular matrix preparations require addition of high concentrations of recombinant growth factors and allow only limited formation of capillary-like structures. Additional advantages of our approach over existing extracellular matrices are the absence of any animal product in the composition hPLG and the possibility of obtaining hPLG from patients to generate homologous scaffolds for re-implantation. This discovery has the potential to accelerate the development of regenerative medicine applications based on implantation of microvascular networks expanded ex vivo or the generation of fully vascularised organs.

Platelet lysate gel and endothelial progenitors stimulate microvascular network formation in vitro: tissue engineering implications

PubMed Central

Fortunato, Tiago M.; Beltrami, Cristina; Emanueli, Costanza; De Bank, Paul A.; Pula, Giordano

2016-01-01

Revascularisation is a key step for tissue regeneration and complete organ engineering. We describe the generation of human platelet lysate gel (hPLG), an extracellular matrix preparation from human platelets able to support the proliferation of endothelial colony forming cells (ECFCs) in 2D cultures and the formation of a complete microvascular network in vitro in 3D cultures. Existing extracellular matrix preparations require addition of high concentrations of recombinant growth factors and allow only limited formation of capillary-like structures. Additional advantages of our approach over existing extracellular matrices are the absence of any animal product in the composition hPLG and the possibility of obtaining hPLG from patients to generate homologous scaffolds for re-implantation. This discovery has the potential to accelerate the development of regenerative medicine applications based on implantation of microvascular networks expanded ex vivo or the generation of fully vascularised organs. PMID:27141997

Multi-scale integration and predictability in resting state brain activity

PubMed Central

Kolchinsky, Artemy; van den Heuvel, Martijn P.; Griffa, Alessandra; Hagmann, Patric; Rocha, Luis M.; Sporns, Olaf; Goñi, Joaquín

2014-01-01

The human brain displays heterogeneous organization in both structure and function. Here we develop a method to characterize brain regions and networks in terms of information-theoretic measures. We look at how these measures scale when larger spatial regions as well as larger connectome sub-networks are considered. This framework is applied to human brain fMRI recordings of resting-state activity and DSI-inferred structural connectivity. We find that strong functional coupling across large spatial distances distinguishes functional hubs from unimodal low-level areas, and that this long-range functional coupling correlates with structural long-range efficiency on the connectome. We also find a set of connectome regions that are both internally integrated and coupled to the rest of the brain, and which resemble previously reported resting-state networks. Finally, we argue that information-theoretic measures are useful for characterizing the functional organization of the brain at multiple scales. PMID:25104933

Highly Stretchable and Transparent Electromagnetic Interference Shielding Film Based on Silver Nanowire Percolation Network for Wearable Electronics Applications.

PubMed

Jung, Jinwook; Lee, Habeom; Ha, Inho; Cho, Hyunmin; Kim, Kyun Kyu; Kwon, Jinhyeong; Won, Phillip; Hong, Sukjoon; Ko, Seung Hwan

2017-12-27

Future electronics are expected to develop into wearable forms, and an adequate stretchability is required for the forthcoming wearable electronics considering various motions occurring in human body. Along with stretchability, transparency can increase both the functionality and esthetic features in future wearable electronics. In this study, we demonstrate, for the first time, a highly stretchable and transparent electromagnetic interference shielding layer for wearable electronic applications with silver nanowire percolation network on elastic poly(dimethylsiloxane) substrate. The proposed stretchable and transparent electromagnetic interference shielding layer shows a high electromagnetic wave shielding effectiveness even under a high tensile strain condition. It is expected for the silver nanowire percolation network-based electromagnetic interference shielding layer to be beyond the conventional electromagnetic interference shielding materials and to broaden its application range to various fields that require optical transparency or nonplanar surface environment, such as biological system, human skin, and wearable electronics.

Human capital, social capital and social exclusion: impacts on the opportunity of households with youth to leave poverty.

PubMed

Wong, Hung

2006-01-01

Based on a sample survey, this paper, analyzes the impact of human capital, social capital and social exclusion on the opportunity of Hong Kong families with youth members to leave poverty. Educational attainment of the youth members and adult family members, as well as the quantity and quality of social networks were found to have significant positive impacts, while social exclusion from the labor market of the adult members was found to have significant negative impact on their opportunity to leave poverty. Among all factors, quality of social network is the most influential. The author suggests that in order to help families out of poverty and enable positive development of youth members, poverty alleviation policies or programs should be targeted to help the youth in poor families to build up a quality social network.

Integrating Cellular Metabolism into a Multiscale Whole-Body Model

PubMed Central

Krauss, Markus; Schaller, Stephan; Borchers, Steffen; Findeisen, Rolf; Lippert, Jörg; Kuepfer, Lars

2012-01-01

Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development. PMID:23133351

A patterned recombinant human IgM guides neurite outgrowth of CNS neurons

PubMed Central

Xu, Xiaohua; Wittenberg, Nathan J.; Jordan, Luke R.; Kumar, Shailabh; Watzlawik, Jens O.; Warrington, Arthur E.; Oh, Sang-Hyun; Rodriguez, Moses

2013-01-01

Matrix molecules convey biochemical and physical guiding signals to neurons in the central nervous system (CNS) and shape the trajectory of neuronal fibers that constitute neural networks. We have developed recombinant human IgMs that bind to epitopes on neural cells, with the aim of treating neurological diseases. Here we test the hypothesis that recombinant human IgMs (rHIgM) can guide neurite outgrowth of CNS neurons. Microcontact printing was employed to pattern rHIgM12 and rHIgM22, antibodies that were bioengineered to have variable regions capable of binding to neurons or oligodendrocytes, respectively. rHIgM12 promoted neuronal attachment and guided outgrowth of neurites from hippocampal neurons. Processes from spinal neurons followed grid patterns of rHIgM12 and formed a physical network. Comparison between rHIgM12 and rHIgM22 suggested the biochemistry that facilitates anchoring the neuronal surfaces is a prerequisite for the function of IgM, and spatial properties cooperate in guiding the assembly of neuronal networks. PMID:23881231

Human neural stem cell-derived cultures in three-dimensional substrates form spontaneously functional neuronal networks.

PubMed

Smith, Imogen; Silveirinha, Vasco; Stein, Jason L; de la Torre-Ubieta, Luis; Farrimond, Jonathan A; Williamson, Elizabeth M; Whalley, Benjamin J

2017-04-01

Differentiated human neural stem cells were cultured in an inert three-dimensional (3D) scaffold and, unlike two-dimensional (2D) but otherwise comparable monolayer cultures, formed spontaneously active, functional neuronal networks that responded reproducibly and predictably to conventional pharmacological treatments to reveal functional, glutamatergic synapses. Immunocytochemical and electron microscopy analysis revealed a neuronal and glial population, where markers of neuronal maturity were observed in the former. Oligonucleotide microarray analysis revealed substantial differences in gene expression conferred by culturing in a 3D vs a 2D environment. Notable and numerous differences were seen in genes coding for neuronal function, the extracellular matrix and cytoskeleton. In addition to producing functional networks, differentiated human neural stem cells grown in inert scaffolds offer several significant advantages over conventional 2D monolayers. These advantages include cost savings and improved physiological relevance, which make them better suited for use in the pharmacological and toxicological assays required for development of stem cell-based treatments and the reduction of animal use in medical research. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Reconstruction and Analysis of Human Kidney-Specific Metabolic Network Based on Omics Data

PubMed Central

Zhang, Ai-Di; Dai, Shao-Xing; Huang, Jing-Fei

2013-01-01

With the advent of the high-throughput data production, recent studies of tissue-specific metabolic networks have largely advanced our understanding of the metabolic basis of various physiological and pathological processes. However, for kidney, which plays an essential role in the body, the available kidney-specific model remains incomplete. This paper reports the reconstruction and characterization of the human kidney metabolic network based on transcriptome and proteome data. In silico simulations revealed that house-keeping genes were more essential than kidney-specific genes in maintaining kidney metabolism. Importantly, a total of 267 potential metabolic biomarkers for kidney-related diseases were successfully explored using this model. Furthermore, we found that the discrepancies in metabolic processes of different tissues are directly corresponding to tissue's functions. Finally, the phenotypes of the differentially expressed genes in diabetic kidney disease were characterized, suggesting that these genes may affect disease development through altering kidney metabolism. Thus, the human kidney-specific model constructed in this study may provide valuable information for the metabolism of kidney and offer excellent insights into complex kidney diseases. PMID:24222897

Transcriptome Dynamics of Developing Photoreceptors in Three‐Dimensional Retina Cultures Recapitulates Temporal Sequence of Human Cone and Rod Differentiation Revealing Cell Surface Markers and Gene Networks

PubMed Central

Kaewkhaw, Rossukon; Kaya, Koray Dogan; Brooks, Matthew; Homma, Kohei; Zou, Jizhong; Chaitankar, Vijender; Rao, Mahendra

2015-01-01

Abstract The derivation of three‐dimensional (3D) stratified neural retina from pluripotent stem cells has permitted investigations of human photoreceptors. We have generated a H9 human embryonic stem cell subclone that carries a green fluorescent protein (GFP) reporter under the control of the promoter of cone‐rod homeobox (CRX), an established marker of postmitotic photoreceptor precursors. The CRXp‐GFP reporter replicates endogenous CRX expression in vitro when the H9 subclone is induced to form self‐organizing 3D retina‐like tissue. At day 37, CRX+ photoreceptors appear in the basal or middle part of neural retina and migrate to apical side by day 67. Temporal and spatial patterns of retinal cell type markers recapitulate the predicted sequence of development. Cone gene expression is concomitant with CRX, whereas rod differentiation factor neural retina leucine zipper protein (NRL) is first observed at day 67. At day 90, robust expression of NRL and its target nuclear receptor NR2E3 is evident in many CRX+ cells, while minimal S‐opsin and no rhodopsin or L/M‐opsin is present. The transcriptome profile, by RNA‐seq, of developing human photoreceptors is remarkably concordant with mRNA and immunohistochemistry data available for human fetal retina although many targets of CRX, including phototransduction genes, exhibit a significant delay in expression. We report on temporal changes in gene signatures, including expression of cell surface markers and transcription factors; these expression changes should assist in isolation of photoreceptors at distinct stages of differentiation and in delineating coexpression networks. Our studies establish the first global expression database of developing human photoreceptors, providing a reference map for functional studies in retinal cultures. Stem Cells 2015;33:3504–3518 PMID:26235913

The social brain: scale-invariant layering of Erdős-Rényi networks in small-scale human societies.

PubMed

Harré, Michael S; Prokopenko, Mikhail

2016-05-01

The cognitive ability to form social links that can bind individuals together into large cooperative groups for safety and resource sharing was a key development in human evolutionary and social history. The 'social brain hypothesis' argues that the size of these social groups is based on a neurologically constrained capacity for maintaining long-term stable relationships. No model to date has been able to combine a specific socio-cognitive mechanism with the discrete scale invariance observed in ethnographic studies. We show that these properties result in nested layers of self-organizing Erdős-Rényi networks formed by each individual's ability to maintain only a small number of social links. Each set of links plays a specific role in the formation of different social groups. The scale invariance in our model is distinct from previous 'scale-free networks' studied using much larger social groups; here, the scale invariance is in the relationship between group sizes, rather than in the link degree distribution. We also compare our model with a dominance-based hierarchy and conclude that humans were probably egalitarian in hunter-gatherer-like societies, maintaining an average maximum of four or five social links connecting all members in a largest social network of around 132 people. © 2016 The Author(s).

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

PubMed Central

Hartman, John L.; Stisher, Chandler; Outlaw, Darryl A.; Guo, Jingyu; Shah, Najaf A.; Tian, Dehua; Santos, Sean M.; Rodgers, John W.; White, Richard A.

2015-01-01

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease. PMID:25668739

Integrated inference and evaluation of host–fungi interaction networks

PubMed Central

Remmele, Christian W.; Luther, Christian H.; Balkenhol, Johannes; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus T.

2015-01-01

Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host–pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host–fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen–host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi–human and fungi–mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host–fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host–fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host–fungi transcriptome and proteome data. PMID:26300851

Real-time object-to-features vectorisation via Siamese neural networks

NASA Astrophysics Data System (ADS)

Fedorenko, Fedor; Usilin, Sergey

2017-03-01

Object-to-features vectorisation is a hard problem to solve for objects that can be hard to distinguish. Siamese and Triplet neural networks are one of the more recent tools used for such task. However, most networks used are very deep networks that prove to be hard to compute in the Internet of Things setting. In this paper, a computationally efficient neural network is proposed for real-time object-to-features vectorisation into a Euclidean metric space. We use L2 distance to reflect feature vector similarity during both training and testing. In this way, feature vectors we develop can be easily classified using K-Nearest Neighbours classifier. Such approach can be used to train networks to vectorise such "problematic" objects like images of human faces, keypoint image patches, like keypoints on Arctic maps and surrounding marine areas.

Individual heterogeneity generating explosive system network dynamics.

PubMed

Manrique, Pedro D; Johnson, Neil F

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Detecting and evaluating communities in complex human and biological networks

NASA Astrophysics Data System (ADS)

Morrison, Greg; Mahadevan, L.

2012-02-01

We develop a simple method for detecting the community structure in a network can by utilizing a measure of closeness between nodes. This approach readily leads to a method of coarse graining the network, which allows the detection of the natural hierarchy (or hierarchies) of community structure without appealing to an unknown resolution parameter. The closeness measure can also be used to evaluate the robustness of an individual node's assignment to its community (rather than evaluating only the quality of the global structure). Each of these methods in community detection and evaluation are illustrated using a variety of real world networks of either biological or sociological importance and illustrate the power and flexibility of the approach.

Self-supported fibrin-polyvinyl alcohol interpenetrating polymer networks: an easily handled and rehydratable biomaterial.

PubMed

Bidault, Laurent; Deneufchatel, Marie; Vancaeyzeele, Cédric; Fichet, Odile; Larreta-Garde, Véronique

2013-11-11

A fibrin hydrogel at physiological concentration (5 mg/mL) was associated with polyvinyl alcohol (PVA) inside an interpenetrating polymer networks (IPN) architecture. Previously, PVA has been modified with methacrylate functions in order to cross-link it by free-radical polymerization. The fibrin network was synthesized by the enzymatic hydrolysis of fibrinogen by thrombin. The resulting self-supported materials simultaneously exhibit the properties of the fibrin hydrogel and those of the synthetic polymer network. Their storage modulus is 50-fold higher than that of the fibrin hydrogel and they are completely rehydratable. These materials are noncytotoxic toward human fibroblast and the fibrin present on the surface of PVAm-based IPNs favors cell development.

Individual heterogeneity generating explosive system network dynamics

NASA Astrophysics Data System (ADS)

Manrique, Pedro D.; Johnson, Neil F.

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

NASA Astrophysics Data System (ADS)

Rosário, R. S.; Cardoso, P. T.; Muñoz, M. A.; Montoya, P.; Miranda, J. G. V.

2015-12-01

The major aim of this work was to propose a new association method known as Motif-Synchronization. This method was developed to provide information about the synchronization degree and direction between two nodes of a network by counting the number of occurrences of some patterns between any two time series. The second objective of this work was to present a new methodology for the analysis of dynamic brain networks, by combining the Time-Varying Graph (TVG) method with a directional association method. We further applied the new algorithms to a set of human electroencephalogram (EEG) signals to perform a dynamic analysis of the brain functional networks (BFN).

Intersectoral planning for city health development.

PubMed

Green, Geoff

2012-04-01

The article reviews the evolution and process of city health development planning (CHDP) in municipalities participating in the European Network of Healthy Cities organized by the European Region of the World Health Organization. The concept of CHDP combines elements from three theoretical domains: (a) health development, (b) city governance, and (c) urban planning. The setting was the 77 cities which participated in Phase IV (2003-2008) of the network. Evidence was gathered principally from a general evaluation questionnaire sent to all network cities. CHDPs are strategic documents giving direction to municipalities and partner agencies. Analysis revealed a trend away from "classic" CHDPs with a primary focus on health development towards ensuring a health dimension to other sector plans, and into the overarching strategies of city governments. Linked to the Phase IV priority themes of Healthy aging and healthy urban planning, cities further developed the concept and application of human-centered sustainability. More work is required to utilize cost-benefit analysis and health impact assessment to unmask the synergies between health and economic prosperity.

Experimental Studies in a Reconfigurable C4 Test-bed for Network Enabled Capability

DTIC Science & Technology

2006-06-01

Cross1, Dr R. Houghton1, and Mr R. McMaster1 Defence Technology Centre for Human factors Integration (DTC HFI ) BITlab, School of Engineering and Design...NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Defence Technology Centre for Human factors Integration (DTC HFI ) BITlab, School of...studies into NEC by the Human Factors Integration Defence Technology Centre ( HFI -DTC). DEVELOPMENT OF THE TESTBED In brief, the C4 test-bed

Structure and function of complex brain networks

PubMed Central

Sporns, Olaf

2013-01-01

An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a “rich club,” centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed. PMID:24174898

Does human migration affect international trade? A complex-network perspective.

PubMed

Fagiolo, Giorgio; Mastrorillo, Marina

2014-01-01

This paper explores the relationships between international human migration and merchandise trade, using a complex-network approach. We firstly compare the topological structure of worldwide networks of human migration and bilateral trade over the period 1960-2000. Next, we ask whether the position of any pair of countries in the migration network affects their bilateral trade flows. We show that: (i) both weighted and binary versions of the networks of international migration and trade are strongly correlated; (ii) such correlations can be mostly explained by country economic/demographic size and geographical distance; and (iii) pairs of countries that are more central in the international-migration network trade more. Our findings suggest that bilateral trade between any two countries is not only affected by the presence of migrants from either countries but also by their relative embeddedness in the complex web of corridors making up the network of international human migration.

Does Human Migration Affect International Trade? A Complex-Network Perspective

PubMed Central

Fagiolo, Giorgio; Mastrorillo, Marina

2014-01-01

This paper explores the relationships between international human migration and merchandise trade using a complex-network approach. We firstly compare the topological structure of worldwide networks of human migration and bilateral trade over the period 1960–2000. Next, we ask whether pairs of countries that are more central in the migration network trade more. We show that: (i) the networks of international migration and trade are strongly correlated, and such correlation can be mostly explained by country economic/demographic size and geographical distance; (ii) centrality in the international-migration network boosts bilateral trade; (iii) intensive forms of country centrality are more trade enhancing than their extensive counterparts. Our findings suggest that bilateral trade between any two countries is not only affected by the presence of migrants from either countries, but also by their relative embeddedness in the complex web of corridors making up the network of international human migration. PMID:24828376

Gene expression links functional networks across cortex and striatum.

PubMed

Anderson, Kevin M; Krienen, Fenna M; Choi, Eun Young; Reinen, Jenna M; Yeo, B T Thomas; Holmes, Avram J

2018-04-12

The human brain is comprised of a complex web of functional networks that link anatomically distinct regions. However, the biological mechanisms supporting network organization remain elusive, particularly across cortical and subcortical territories with vastly divergent cellular and molecular properties. Here, using human and primate brain transcriptional atlases, we demonstrate that spatial patterns of gene expression show strong correspondence with limbic and somato/motor cortico-striatal functional networks. Network-associated expression is consistent across independent human datasets and evolutionarily conserved in non-human primates. Genes preferentially expressed within the limbic network (encompassing nucleus accumbens, orbital/ventromedial prefrontal cortex, and temporal pole) relate to risk for psychiatric illness, chloride channel complexes, and markers of somatostatin neurons. Somato/motor associated genes are enriched for oligodendrocytes and markers of parvalbumin neurons. These analyses indicate that parallel cortico-striatal processing channels possess dissociable genetic signatures that recapitulate distributed functional networks, and nominate molecular mechanisms supporting cortico-striatal circuitry in health and disease.

Establishment of Stereo Multi-sensor Network for Giant Landslide Monitoring and its Deploy in Xishan landslide, Sichuan, China.

NASA Astrophysics Data System (ADS)

Liu, C.; Lu, P.; WU, H.

2015-12-01

Landslide is one of the most destructive natural disasters, which severely affects human lives as well as the safety of personal properties and public infrastructures. Monitoring and predicting landslide movements can keep an adequate safety level for human beings in those situations. This paper indicated a newly developed Stereo Multi-sensor Landslide Monitoring Network (SMSLMN) based on a uniform temporal geo-reference. Actually, early in 2003, SAMOA (Surveillance et Auscultation des Mouvements de Terrain Alpins, French) project was put forwarded as a plan for landslide movements monitoring. However, SAMOA project did not establish a stereo observation network to fully cover the surface and internal part of landslide. SMSLMN integrated various sensors, including space-borne, airborne, in-situ and underground sensors, which can quantitatively monitor the slide-body and obtain portent information of movement in high frequency with high resolution. The whole network has been deployed at the Xishan landslide, Sichuan, P.R.China. According to various characteristic of stereo monitoring sensors, observation capabilities indicators for different sensors were proposed in order to obtain the optimal sensors combination groups and observation strategy. Meanwhile, adaptive networking and reliable data communication methods were developed to apply intelligent observation and sensor data transmission. Some key technologies, such as signal amplification and intelligence extraction technology, data access frequency adaptive adjustment technology, different sensor synchronization control technology were developed to overcome the problems in complex observation environment. The collaboratively observation data have been transferred to the remote data center where is thousands miles away from the giant landslide spot. These data were introduced into the landslide stability analysis model, and some primary conclusion will be achieved at the end of paper.

Predicting Human Protein Subcellular Locations by the Ensemble of Multiple Predictors via Protein-Protein Interaction Network with Edge Clustering Coefficients

PubMed Central

Du, Pufeng; Wang, Lusheng

2014-01-01

One of the fundamental tasks in biology is to identify the functions of all proteins to reveal the primary machinery of a cell. Knowledge of the subcellular locations of proteins will provide key hints to reveal their functions and to understand the intricate pathways that regulate biological processes at the cellular level. Protein subcellular location prediction has been extensively studied in the past two decades. A lot of methods have been developed based on protein primary sequences as well as protein-protein interaction network. In this paper, we propose to use the protein-protein interaction network as an infrastructure to integrate existing sequence based predictors. When predicting the subcellular locations of a given protein, not only the protein itself, but also all its interacting partners were considered. Unlike existing methods, our method requires neither the comprehensive knowledge of the protein-protein interaction network nor the experimentally annotated subcellular locations of most proteins in the protein-protein interaction network. Besides, our method can be used as a framework to integrate multiple predictors. Our method achieved 56% on human proteome in absolute-true rate, which is higher than the state-of-the-art methods. PMID:24466278

Optimal spatiotemporal representation of multichannel EEG for recognition of brain states associated with distinct visual stimulus

NASA Astrophysics Data System (ADS)

Hramov, Alexander; Musatov, Vyacheslav Yu.; Runnova, Anastasija E.; Efremova, Tatiana Yu.; Koronovskii, Alexey A.; Pisarchik, Alexander N.

2018-04-01

In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.

Mobile Network Data for Public Health: Opportunities and Challenges

PubMed Central

Oliver, Nuria; Matic, Aleksandar; Frias-Martinez, Enrique

2015-01-01

The ubiquity of mobile phones worldwide is generating an unprecedented amount of human behavioral data both at an individual and aggregated levels. The study of this data as a rich source of information about human behavior emerged almost a decade ago. Since then, it has grown into a fertile area of research named computational social sciences with a wide variety of applications in different fields such as social networks, urban and transport planning, economic development, emergency relief, and, recently, public health. In this paper, we briefly describe the state of the art on using mobile phone data for public health, and present the opportunities and challenges that this kind of data presents for public health. PMID:26301211

Intelligence Applied to Air Vehicles

NASA Technical Reports Server (NTRS)

Rosen, Robert; Gross, Anthony R.; Fletcher, L. Skip; Zornetzer, Steven (Technical Monitor)

2000-01-01

The exponential growth in information technology has provided the potential for air vehicle capabilities that were previously unavailable to mission and vehicle designers. The increasing capabilities of computer hardware and software, including new developments such as neural networks, provide a new balance of work between humans and machines. This paper will describe several NASA projects, and review results and conclusions from ground and flight investigations where vehicle intelligence was developed and applied to aeronautical and space systems. In the first example, flight results from a neural network flight control demonstration will be reviewed. Using, a highly-modified F-15 aircraft, a NASA/Dryden experimental flight test program has demonstrated how the neural network software can correctly identify and respond to changes in aircraft stability and control characteristics. Using its on-line learning capability, the neural net software would identify that something in the vehicle has changed, then reconfigure the flight control computer system to adapt to those changes. The results of the Remote Agent software project will be presented. This capability will reduce the cost of future spacecraft operations as computers become "thinking" partners along with humans. In addition, the paper will describe the objectives and plans for the autonomous airplane program and the autonomous rotorcraft project. Technologies will also be developed.

Human factors for capacity building: lessons learned from the OpenMRS implementers network.

PubMed

Seebregts, C J; Mamlin, B W; Biondich, P G; Fraser, H S F; Wolfe, B A; Jazayeri, D; Miranda, J; Blaya, J; Sinha, C; Bailey, C T; Kanter, A S

2010-01-01

The overall objective of this project was to investigate ways to strengthen the OpenMRS community by (i) developing capacity and implementing a network focusing specifically on the needs of OpenMRS implementers, (ii) strengthening community-driven aspects of OpenMRS and providing a dedicated forum for implementation-specific issues, and; (iii) providing regional support for OpenMRS implementations as well as mentorship and training. The methods used included (i) face-to-face networking using meetings and workshops; (ii) online collaboration tools, peer support and mentorship programmes; (iii) capacity and community development programmes, and; (iv) community outreach programmes. The community-driven approach, combined with a few simple interventions, has been a key factor in the growth and success of the OpenMRS Implementers Network. It has contributed to implementations in at least twenty-three different countries using basic online tools; and provided mentorship and peer support through an annual meeting, workshops and an internship program. The OpenMRS Implementers Network has formed collaborations with several other open source networks and is evolving regional OpenMRS Centres of Excellence to provide localized support for OpenMRS development and implementation. These initiatives are increasing the range of functionality and sustainability of open source software in the health domain, resulting in improved adoption and enterprise-readiness. Social organization and capacity development activities are important in growing a successful community-driven open source software model.

Applications of spatial statistical network models to stream data

USGS Publications Warehouse

Isaak, Daniel J.; Peterson, Erin E.; Ver Hoef, Jay M.; Wenger, Seth J.; Falke, Jeffrey A.; Torgersen, Christian E.; Sowder, Colin; Steel, E. Ashley; Fortin, Marie-Josée; Jordan, Chris E.; Ruesch, Aaron S.; Som, Nicholas; Monestiez, Pascal

2014-01-01

Streams and rivers host a significant portion of Earth's biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management. Most statistical techniques applied to data measured on stream networks were developed for terrestrial applications and are not optimized for streams. A new class of spatial statistical model, based on valid covariance structures for stream networks, can be used with many common types of stream data (e.g., water quality attributes, habitat conditions, biological surveys) through application of appropriate distributions (e.g., Gaussian, binomial, Poisson). The spatial statistical network models account for spatial autocorrelation (i.e., nonindependence) among measurements, which allows their application to databases with clustered measurement locations. Large amounts of stream data exist in many areas where spatial statistical analyses could be used to develop novel insights, improve predictions at unsampled sites, and aid in the design of efficient monitoring strategies at relatively low cost. We review the topic of spatial autocorrelation and its effects on statistical inference, demonstrate the use of spatial statistics with stream datasets relevant to common research and management questions, and discuss additional applications and development potential for spatial statistics on stream networks. Free software for implementing the spatial statistical network models has been developed that enables custom applications with many stream databases.

A common brain network links development, aging, and vulnerability to disease.

PubMed

Douaud, Gwenaëlle; Groves, Adrian R; Tamnes, Christian K; Westlye, Lars Tjelta; Duff, Eugene P; Engvig, Andreas; Walhovd, Kristine B; James, Anthony; Gass, Achim; Monsch, Andreas U; Matthews, Paul M; Fjell, Anders M; Smith, Stephen M; Johansen-Berg, Heidi

2014-12-09

Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8-85 y) reveals a largely--but not only--transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer's disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer's disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer's disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.

Sustained UK marine observations. Where have we been? Where are we now? Where are we going?

PubMed Central

Owens, Nicholas J. P.

2014-01-01

This introduction traces the earliest interaction of ancient humans with their marine environment, through marine explorations in the Middle Ages and Renaissance, to the development of early marine science in the Enlightenment. This sets the scene for how marine observations developed in the modern era and explains the status of today's marine observation networks. The paper concludes with an assessment of the future needs and constraints of sustained marine observation networks and suggests the lessons from a long history might be the key to the future. PMID:25157193

Tuning the developing brain to social signals of emotions

PubMed Central

Leppänen, Jukka M.; Nelson, Charles A.

2010-01-01

PREFACE Humans in diverse cultures develop a similar capacity to recognize the emotional signals of different facial expressions. This capacity is mediated by a brain network that involves emotion-related brain circuits and higher-level visual representation areas. Recent studies suggest that the key components of this network begin to emerge early in life. The studies also suggest that initial biases in emotion-related brain circuits and the early coupling of these circuits and cortical perceptual areas provides a foundation for a rapid acquisition of representations of those facial features that denote specific emotions. PMID:19050711

The relation between global migration and trade networks

NASA Astrophysics Data System (ADS)

Sgrignoli, Paolo; Metulini, Rodolfo; Schiavo, Stefano; Riccaboni, Massimo

2015-01-01

In this paper we develop a methodology to analyze and compare multiple global networks, focusing our analysis on the relation between human migration and trade. First, we identify the subset of products for which the presence of a community of migrants significantly increases trade intensity, where to assure comparability across networks we apply a hypergeometric filter that lets us identify those links which intensity is significantly higher than expected. Next, proposing a new way to define country neighbors based on the most intense links in the trade network, we use spatial econometrics techniques to measure the effect of migration on international trade, while controlling for network interdependences. Overall, we find that migration significantly boosts trade across countries and we are able to identify product categories for which this effect is particularly strong.

Resilience of Natural Gas Networks during Conflicts, Crises and Disruptions

PubMed Central

Carvalho, Rui; Buzna, Lubos; Bono, Flavio; Masera, Marcelo; Arrowsmith, David K.; Helbing, Dirk

2014-01-01

Human conflict, geopolitical crises, terrorist attacks, and natural disasters can turn large parts of energy distribution networks offline. Europe's current gas supply network is largely dependent on deliveries from Russia and North Africa, creating vulnerabilities to social and political instabilities. During crises, less delivery may mean greater congestion, as the pipeline network is used in ways it has not been designed for. Given the importance of the security of natural gas supply, we develop a model to handle network congestion on various geographical scales. We offer a resilient response strategy to energy shortages and quantify its effectiveness for a variety of relevant scenarios. In essence, Europe's gas supply can be made robust even to major supply disruptions, if a fair distribution strategy is applied. PMID:24621655

Decentralized sensor fusion for Ubiquitous Networking Robotics in Urban Areas.

PubMed

Sanfeliu, Alberto; Andrade-Cetto, Juan; Barbosa, Marco; Bowden, Richard; Capitán, Jesús; Corominas, Andreu; Gilbert, Andrew; Illingworth, John; Merino, Luis; Mirats, Josep M; Moreno, Plínio; Ollero, Aníbal; Sequeira, João; Spaan, Matthijs T J

2010-01-01

In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking Robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensor fusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted.

Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

PubMed Central

Barrett, Louise; Henzi, S. Peter; Lusseau, David

2012-01-01

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution. PMID:22734054

Empirical modeling for intelligent, real-time manufacture control

NASA Technical Reports Server (NTRS)

Xu, Xiaoshu

1994-01-01

Artificial neural systems (ANS), also known as neural networks, are an attempt to develop computer systems that emulate the neural reasoning behavior of biological neural systems (e.g. the human brain). As such, they are loosely based on biological neural networks. The ANS consists of a series of nodes (neurons) and weighted connections (axons) that, when presented with a specific input pattern, can associate specific output patterns. It is essentially a highly complex, nonlinear, mathematical relationship or transform. These constructs have two significant properties that have proven useful to the authors in signal processing and process modeling: noise tolerance and complex pattern recognition. Specifically, the authors have developed a new network learning algorithm that has resulted in the successful application of ANS's to high speed signal processing and to developing models of highly complex processes. Two of the applications, the Weld Bead Geometry Control System and the Welding Penetration Monitoring System, are discussed in the body of this paper.

Vulnerability of complex networks

NASA Astrophysics Data System (ADS)

Mishkovski, Igor; Biey, Mario; Kocarev, Ljupco

2011-01-01

We consider normalized average edge betweenness of a network as a metric of network vulnerability. We suggest that normalized average edge betweenness together with is relative difference when certain number of nodes and/or edges are removed from the network is a measure of network vulnerability, called vulnerability index. Vulnerability index is calculated for four synthetic networks: Erdős-Rényi (ER) random networks, Barabási-Albert (BA) model of scale-free networks, Watts-Strogatz (WS) model of small-world networks, and geometric random networks. Real-world networks for which vulnerability index is calculated include: two human brain networks, three urban networks, one collaboration network, and two power grid networks. We find that WS model of small-world networks and biological networks (human brain networks) are the most robust networks among all networks studied in the paper.

Nanosecond UV lasers stimulate transient Ca2+ elevations in human hNT astrocytes.

PubMed

Raos, B J; Graham, E S; Unsworth, C P

2017-06-01

Astrocytes respond to various stimuli resulting in intracellular Ca 2+ signals that can propagate through organized functional networks. Recent literature calls for the development of techniques that can stimulate astrocytes in a fast and highly localized manner to emulate more closely the characteristics of astrocytic Ca 2+ signals in vivo. In this article we demonstrate, for the first time, how nanosecond UV lasers are capable of reproducibly stimulating Ca 2+ transients in human hNT astrocytes. We report that laser pulses with a beam energy of 4-29 µJ generate transient increases in cytosolic Ca 2+ . These Ca 2+ transients then propagate to adjacent astrocytes as intercellular Ca 2+ waves. We propose that nanosecond laser stimulation provides a valuable tool for enabling the study of Ca 2+ dynamics in human astrocytes at both a single cell and network level. Compared to previously developed techniques nanosecond laser stimulation has the advantage of not requiring loading of photo-caged or -sensitising agents, is non-contact, enables stimulation with a high spatiotemporal resolution and is comparatively cost effective.

Evidence for a cerebral cortical thickness network anti-correlated with amygdalar volume in healthy youths: implications for the neural substrates of emotion regulation.

PubMed

Albaugh, Matthew D; Ducharme, Simon; Collins, D Louis; Botteron, Kelly N; Althoff, Robert R; Evans, Alan C; Karama, Sherif; Hudziak, James J

2013-05-01

Recent functional connectivity studies have demonstrated that, in resting humans, activity in a dorsally-situated neocortical network is inversely associated with activity in the amygdalae. Similarly, in human neuroimaging studies, aspects of emotion regulation have been associated with increased activity in dorsolateral, dorsomedial, orbital and ventromedial prefrontal regions, as well as concomitant decreases in amygdalar activity. These findings indicate the presence of two countervailing systems in the human brain that are reciprocally related: a dorsally-situated cognitive control network, and a ventrally-situated limbic network. We investigated the extent to which this functional reciprocity between limbic and dorsal neocortical regions is recapitulated from a purely structural standpoint. Specifically, we hypothesized that amygdalar volume would be related to cerebral cortical thickness in cortical regions implicated in aspects of emotion regulation. In 297 typically developing youths (162 females, 135 males; 572 MRIs), the relationship between cortical thickness and amygdalar volume was characterized. Amygdalar volume was found to be inversely associated with thickness in bilateral dorsolateral and dorsomedial prefrontal, inferior parietal, as well as bilateral orbital and ventromedial prefrontal cortices. Our findings are in line with previous work demonstrating that a predominantly dorsally-centered neocortical network is reciprocally related to core limbic structures such as the amygdalae. Future research may benefit from investigating the extent to which such cortical-limbic morphometric relations are qualified by the presence of mood and anxiety psychopathology. Copyright © 2012 Elsevier Inc. All rights reserved.

Visualization of the microcirculatory network in skin by high frequency optoacoustic mesoscopy

NASA Astrophysics Data System (ADS)

Schwarz, Mathias; Aguirre, Juan; Buehler, Andreas; Omar, Murad; Ntziachristos, Vasilis

2015-07-01

Optoacoustic (photoacoustic) imaging has a high potential for imaging melanin-rich structures in skin and the microvasculature of the dermis due to the natural chromophores (de)oxyhemoglobin, and melanin. The vascular network in human dermis comprises a large network of arterioles, capillaries, and venules, ranging from 5 μm to more than 100 μm in diameter. The frequency spectrum of the microcirculatory network in human skin is intrinsically broadband, due to the large variety in size of absorbers. In our group we have developed raster-scan optoacoustic mesoscopy (RSOM) that applies a 100 MHz transducer with ultra-wide bandwidth in raster-scan mode achieving lateral resolution of 18 μm. In this study, we applied high frequency RSOM to imaging human skin in a healthy volunteer. We analyzed the frequency spectrum of anatomical structures with respect to depth and show that frequencies >60 MHz contain valuable information of structures in the epidermis and the microvasculature of the papillary dermis. We illustrate that RSOM is capable of visualizing the fine vascular network at and beneath the epidermal-dermal junction, revealing the vascular fingerprint of glabrous skin, as well as the larger venules deeper inside the dermis. We evaluate the ability of the RSOM system in measuring epidermal thickness in both hairy and glabrous skin. Finally, we showcase the capability of RSOM in visualizing benign nevi that will potentially help in imaging the penetration depth of melanoma.

TP53 mutations, expression and interaction networks in human cancers

PubMed Central

Wang, Xiaosheng; Sun, Qingrong

2017-01-01

Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers. PMID:27880943

TP53 mutations, expression and interaction networks in human cancers.

PubMed

Wang, Xiaosheng; Sun, Qingrong

2017-01-03

Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers.

Developing the Master Educator: Cross Disciplinary Teaching Scholars Program for Human and Veterinary Medical Faculty

ERIC Educational Resources Information Center

Srinivasan, Malathi; Pratt, Daniel D.; Collins, John; Bowe, Constance M.; Stevenson, Frazier T.; Pinney, Stephen J.; Wilkes, Michael S.

2007-01-01

Objective: At the University of California, Davis (UCD), the authors sought to develop an institutional network of reflective educational leaders. The authors wanted to enhance faculty understanding of medical education's complexity, and improve educators' effectiveness as regional/national leaders. Methods: The UCD Teaching Scholars Program is a…

Complexity, Chaos, and Nonlinear Dynamics: A New Perspective on Career Development Theory

ERIC Educational Resources Information Center

Bloch, Deborah P.

2005-01-01

The author presents a theory of career development drawing on nonlinear dynamics and chaos and complexity theories. Career is presented as a complex adaptive entity, a fractal of the human entity. Characteristics of complex adaptive entities, including (a) autopiesis, or self-regeneration; (b) open exchange; (c) participation in networks; (d)…

Development of Support Service for Prevention and Recovery from Dementia and Science of Lethe

NASA Astrophysics Data System (ADS)

Otake, Mihoko

This paper proposes multiscale service design method through the development of support service for prevention and recovery from dementia towards science of lethe. Proposed multiscale service model consists of tool, event, human, network, style and rule. Service elements at different scales are developed according to the model. Firstly, the author proposes and practices coimagination method as an ``event'', which is expected to prevent the progress of cognitive impairment. Coimagination support system was developed as a ``tool''. Experimental results suggest the effective activation of episodic memory, division of attention, and planning function of participants by the measurement of cognitive activities during the coimagination. Then, Fonobono Research Institute was established as a ''network'' for ``human'' who studies coimagination, which is a multisector research organization including elderly people living around Kashiwa city, companies including instrument and welfare companies, Kashiwa city and Chiba prefecture, researchers of the University of Tokyo. The institute proposes and realizes lifelong research as a novel life ``style'' for elderly people, and discusses life with two rounds as an innovative ``rule'' for social system of aged society.

A Neural Network Model of the Effects of Entrenchment and Memory Development on Grammatical Gender Learning

ERIC Educational Resources Information Center

Monner, Derek; Vatz, Karen; Morini, Giovanna; Hwang, So-One; DeKeyser, Robert

2013-01-01

To investigate potential causes of L2 performance deficits that correlate with age of onset, we use a computational model to explore the individual contributions of L1 entrenchment and aspects of memory development. Since development and L1 entrenchment almost invariably coincide, studying them independently is seldom possible in humans. To avoid…

Human-computer interaction reflected in the design of user interfaces for general practitioners.

PubMed

Stoicu-Tivadar, Lacramioara; Stoicu-Tivadar, Vasile

2006-01-01

To address the problem of properly built health information systems in general practice as an important issue for their approval and use in clinical practice. We present how a national general practitioner (GP) network was built, put in practice and several results of its activity seen from the clinician's and the software application team's points of view. We used a multi-level incremental development appropriate for the conditions of the required information system. After the development of the first version of the software components (based on rapid prototyping) of the sentinel network, a questionnaire addressed the needs and improvements required by the health professionals. Based on the answers, the functionality of the system and the interface were improved regarding the real needs expressed by the end-users. The network is functional and the collected data from the network are being processed using statistical methods. The academic software team developed a GP application that is well received by the GPs in the network, as resulted from the survey and discussions during the training period. As an added confirmation, several GPs outside the network enrolled after seeing the software at work. Another confirmation that we did a good job was that after the final presentation of the results of the project a representative from the Romanian Society for Cardiology expressed the wish of this society to access the data yielded by the network.

Quantifying the Digital Divide: A Scientific Overview of Network Connectivity and Grid Infrastructure in South Asian Countries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khan, Shahryar Muhammad; /SLAC /NUST, Rawalpindi; Cottrell, R.Les

2007-10-30

The future of Computing in High Energy Physics (HEP) applications depends on both the Network and Grid infrastructure. South Asian countries such as India and Pakistan are making significant progress by building clusters as well as improving their network infrastructure However to facilitate the use of these resources, they need to manage the issues of network connectivity to be among the leading participants in Computing for HEP experiments. In this paper we classify the connectivity for academic and research institutions of South Asia. The quantitative measurements are carried out using the PingER methodology; an approach that induces minimal ICMP trafficmore » to gather active end-to-end network statistics. The PingER project has been measuring the Internet performance for the last decade. Currently the measurement infrastructure comprises of over 700 hosts in more than 130 countries which collectively represents approximately 99% of the world's Internet-connected population. Thus, we are well positioned to characterize the world's connectivity. Here we present the current state of the National Research and Educational Networks (NRENs) and Grid Infrastructure in the South Asian countries and identify the areas of concern. We also present comparisons between South Asia and other developing as well as developed regions. We show that there is a strong correlation between the Network performance and several Human Development indices.« less

Dissociable changes in functional network topology underlie early category learning and development of automaticity

PubMed Central

Soto, Fabian A.; Bassett, Danielle S.; Ashby, F. Gregory

2016-01-01

Recent work has shown that multimodal association areas–including frontal, temporal and parietal cortex–are focal points of functional network reconfiguration during human learning and performance of cognitive tasks. On the other hand, neurocomputational theories of category learning suggest that the basal ganglia and related subcortical structures are focal points of functional network reconfiguration during early learning of some categorization tasks, but become less so with the development of automatic categorization performance. Using a combination of network science and multilevel regression, we explore how changes in the connectivity of small brain regions can predict behavioral changes during training in a visual categorization task. We find that initial category learning, as indexed by changes in accuracy, is predicted by increasingly efficient integrative processing in subcortical areas, with higher functional specialization, more efficient integration across modules, but a lower cost in terms of redundancy of information processing. The development of automaticity, as indexed by changes in the speed of correct responses, was predicted by lower clustering (particularly in subcortical areas), higher strength (highest in cortical areas) and higher betweenness centrality. By combining neurocomputational theories and network scientific methods, these results synthesize the dissociative roles of multimodal association areas and subcortical structures in the development of automaticity during category learning. PMID:27453156

Cardiac muscle regeneration: lessons from development

PubMed Central

Mercola, Mark; Ruiz-Lozano, Pilar; Schneider, Michael D.

2011-01-01

The adult human heart is an ideal target for regenerative intervention since it does not functionally restore itself after injury yet has a modest regenerative capacity that could be enhanced by innovative therapies. Adult cardiac cells with regenerative potential share gene expression signatures with early fetal progenitors that give rise to multiple cardiac cell types, suggesting that the evolutionarily conserved regulatory networks that drive embryonic heart development might also control aspects of regeneration. Here we discuss commonalities of development and regeneration, and the application of the rich developmental biology heritage to achieve therapeutic regeneration of the human heart. PMID:21325131

“Theory of Food” as a Neurocognitive Adaptation

PubMed Central

Allen, John S.

2011-01-01

Human adult cognition emerges over the course of development via the interaction of multiple critical neurocognitive networks. These networks evolved in response to various selection pressures, many of which were modified or intensified by the intellectual, technological, and socio-cultural environments that arose in connection with the evolution of genus Homo. Networks related to language and theory of mind clearly play an important role in adult cognition. Given the critical importance of food to both basic survival and cultural interaction, a “theory of food” (analogous to theory of mind) may represent another complex network essential for normal cognition. I propose that theory of food evolved as an internal, cognitive representation of our diets in our minds. Like other complex cognitive abilities, it relies on complex and overlapping dedicated neural networks that develop in childhood under familial and cultural influences. Normative diets are analogous to first languages in that they are acquired without overt teaching; they are also difficult to change or modify once a critical period in development is passed. Theory of food suggests that cognitive activities related to food may be cognitive enhancers, which could have implications for maintaining healthy brain function in aging. PMID:22262561

"Theory of food" as a neurocognitive adaptation.

PubMed

Allen, John S

2012-01-01

Human adult cognition emerges over the course of development via the interaction of multiple critical neurocognitive networks. These networks evolved in response to various selection pressures, many of which were modified or intensified by the intellectual, technological, and sociocultural environments that arose in connection with the evolution of genus Homo. Networks related to language and theory of mind clearly play an important role in adult cognition. Given the critical importance of food to both basic survival and cultural interaction, a "theory of food" (analogous to theory of mind) may represent another complex network essential for normal cognition. I propose that theory of food evolved as an internal, cognitive representation of our diets in our minds. Like other complex cognitive abilities, it relies on complex and overlapping dedicated neural networks that develop in childhood under familial and cultural influences. Normative diets are analogous to first languages in that they are acquired without overt teaching; they are also difficult to change or modify once a critical period in development is passed. Theory of food suggests that cognitive activities related to food may be cognitive enhancers, which could have implications for maintaining healthy brain function in aging. Copyright © 2012 Wiley Periodicals, Inc.

Specialization and Universals in the Development of Reading Skill: How Chinese Research Informs a Universal Science of Reading

PubMed Central

Perfetti, Charles; Cao, Fan; Booth, James

2014-01-01

Understanding Chinese reading is important for identifying the universal aspects of reading, separated from those aspects that are specific to alphabetic writing or to English in particular. Chinese and alphabetic writing make different demands on reading and learning to read, despite reading procedures and their supporting brain networks that are partly universal. Learning to read accommodates the demands of a writing system through the specialization of brain networks that support word identification. This specialization increases with reading development, leading to differences in the brain networks for alphabetic and Chinese reading. We suggest that beyond reading procedures that are partly universal and partly writing-system specific, functional reading universals arise across writing systems in their adaptation to human cognitive abilities. PMID:24744605

Putting Gino's lesson to work: Actor-network theory, enacted humanity, and rehabilitation.

PubMed

Abrams, Thomas; Gibson, Barbara E

2016-02-01

This article argues that rehabilitation enacts a particular understanding of "the human" throughout therapeutic assessment and treatment. Following Michel Callon and Vololona Rabeharisoa's "Gino's Lesson on Humanity," we suggest that this is not simply a top-down process, but is cultivated in the application and response to biomedical frameworks of human ability, competence, and responsibility. The emergence of the human is at once a materially contingent, moral, and interpersonal process. We begin the article by outlining the basics of the actor-network theory that underpins "Gino's Lesson on Humanity." Next, we elucidate its central thesis regarding how disabled personhood emerges through actor-network interactions. Section "Learning Gino's lesson" draws on two autobiographical examples, examining the emergence of humanity through rehabilitation, particularly assessment measures and the responses to them. We conclude by thinking about how rehabilitation and actor-network theory might take this lesson on humanity seriously. © The Author(s) 2016.

Skin-Inspired Multifunctional Autonomic-Intrinsic Conductive Self-Healing Hydrogels with Pressure Sensitivity, Stretchability, and 3D Printability.

PubMed

Darabi, Mohammad Ali; Khosrozadeh, Ali; Mbeleck, Rene; Liu, Yuqing; Chang, Qiang; Jiang, Junzi; Cai, Jun; Wang, Quan; Luo, Gaoxing; Xing, Malcolm

2017-08-01

The advent of conductive self-healing (CSH) hydrogels, a class of novel materials mimicking human skin, may change the trajectory of the industrial process because of their potential applications in soft robots, biomimetic prostheses, and health-monitoring systems. Here, the development of a mechanically and electrically self-healing hydrogel based on physically and chemically cross-linked networks is reported. The autonomous intrinsic self-healing of the hydrogel is attained through dynamic ionic interactions between carboxylic groups of poly(acrylic acid) and ferric ions. A covalent cross-linking is used to support the mechanical structure of the hydrogel. Establishing a fair balance between the chemical and physical cross-linking networks together with the conductive nanostructure of polypyrrole networks leads to a double network hydrogel with bulk conductivity, mechanical and electrical self-healing properties (100% mechanical recovery in 2 min), ultrastretchability (1500%), and pressure sensitivity. The practical potential of CSH hydrogels is further revealed by their application in human motion detection and their 3D-printing performance. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Identifying gene networks underlying the neurobiology of ethanol and alcoholism.

PubMed

Wolen, Aaron R; Miles, Michael F

2012-01-01

For complex disorders such as alcoholism, identifying the genes linked to these diseases and their specific roles is difficult. Traditional genetic approaches, such as genetic association studies (including genome-wide association studies) and analyses of quantitative trait loci (QTLs) in both humans and laboratory animals already have helped identify some candidate genes. However, because of technical obstacles, such as the small impact of any individual gene, these approaches only have limited effectiveness in identifying specific genes that contribute to complex diseases. The emerging field of systems biology, which allows for analyses of entire gene networks, may help researchers better elucidate the genetic basis of alcoholism, both in humans and in animal models. Such networks can be identified using approaches such as high-throughput molecular profiling (e.g., through microarray-based gene expression analyses) or strategies referred to as genetical genomics, such as the mapping of expression QTLs (eQTLs). Characterization of gene networks can shed light on the biological pathways underlying complex traits and provide the functional context for identifying those genes that contribute to disease development.

Miniaturized sensors to monitor simulated lunar locomotion.

PubMed

Hanson, Andrea M; Gilkey, Kelly M; Perusek, Gail P; Thorndike, David A; Kutnick, Gilead A; Grodsinsky, Carlos M; Rice, Andrea J; Cavanagh, Peter R

2011-02-01

Human activity monitoring is a useful tool in medical monitoring, military applications, athletic coaching, and home healthcare. We propose the use of an accelerometer-based system to track crewmember activity during space missions in reduced gravity environments. It is unclear how the partial gravity environment of the Moorn or Mars will affect human locomotion. Here we test a novel analogue of lunar gravity in combination with a custom wireless activity tracking system. A noninvasive wireless accelerometer-based sensor system, the activity tracking device (ATD), was developed. The system has two sensor units; one footwear-mounted and the other waist-mounted near the midlower back. Subjects (N=16) were recruited to test the system in the enhanced Zero Gravity Locomotion Simulator (eZLS) at NASA Glenn Research Center. Data were used to develop an artificial neural network for activity recognition. The eZLS demonstrated the ability to replicate reduced gravity environments. There was a 98% agreement between the ATD and force plate-derived stride times during running (9.7 km x h(-1)) at both 1 g and 1/6 g. A neural network was designed and successfully trained to identify lunar walking, running, hopping, and loping from ATD measurements with 100% accuracy. The eZLS is a suitable tool for examining locomotor activity at simulated lunar gravity. The accelerometer-based ATD system is capable of monitoring human activity and may be suitable for use during remote, long-duration space missions. A neural network has been developed to use data from the ATD to aid in remote activity monitoring.

Generative models for network neuroscience: prospects and promise

PubMed Central

Betzel, Richard F.

2017-01-01

Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here, we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science, broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation. Next, we review generative models of biological neural networks, both at the cellular and large-scale level, and across a variety of species including Caenorhabditis elegans, Drosophila, mouse, rat, cat, macaque and human. We offer a careful treatment of a few relevant distinctions, including differences between generative models and null models, sufficiency and redundancy, inferring and claiming mechanism, and functional and structural connectivity. We close with a discussion of future directions, outlining exciting frontiers both in empirical data collection efforts as well as in method and theory development that, together, further the utility of the generative network modelling approach for network neuroscience. PMID:29187640

Integrative transcriptome network analysis of iPSC-derived neurons from schizophrenia and schizoaffective disorder patients with 22q11.2 deletion.

PubMed

Lin, Mingyan; Pedrosa, Erika; Hrabovsky, Anastasia; Chen, Jian; Puliafito, Benjamin R; Gilbert, Stephanie R; Zheng, Deyou; Lachman, Herbert M

2016-11-15

Individuals with 22q11.2 Deletion Syndrome (22q11.2 DS) are a specific high-risk group for developing schizophrenia (SZ), schizoaffective disorder (SAD) and autism spectrum disorders (ASD). Several genes in the deleted region have been implicated in the development of SZ, e.g., PRODH and DGCR8. However, the mechanistic connection between these genes and the neuropsychiatric phenotype remains unclear. To elucidate the molecular consequences of 22q11.2 deletion in early neural development, we carried out RNA-seq analysis to investigate gene expression in early differentiating human neurons derived from induced pluripotent stem cells (iPSCs) of 22q11.2 DS SZ and SAD patients. Eight cases (ten iPSC-neuron samples in total including duplicate clones) and seven controls (nine in total including duplicate clones) were subjected to RNA sequencing. Using a systems level analysis, differentially expressed genes/gene-modules and pathway of interests were identified. Lastly, we related our findings from in vitro neuronal cultures to brain development by mapping differentially expressed genes to BrainSpan transcriptomes. We observed ~2-fold reduction in expression of almost all genes in the 22q11.2 region in SZ (37 genes reached p-value < 0.05, 36 of which reached a false discovery rate < 0.05). Outside of the deleted region, 745 genes showed significant differences in expression between SZ and control neurons (p < 0.05). Function enrichment and network analysis of the differentially expressed genes uncovered converging evidence on abnormal expression in key functional pathways, such as apoptosis, cell cycle and survival, and MAPK signaling in the SZ and SAD samples. By leveraging transcriptome profiles of normal human brain tissues across human development into adulthood, we showed that the differentially expressed genes converge on a sub-network mediated by CDC45 and the cell cycle, which would be disrupted by the 22q11.2 deletion during embryonic brain development, and another sub-network modulated by PRODH, which could contribute to disruption of brain function during adolescence. This study has provided evidence for disruption of potential molecular events in SZ patient with 22q11.2 deletion and related our findings from in vitro neuronal cultures to functional perturbations that can occur during brain development in SZ.

Gene-Disease Network Analysis Reveals Functional Modules in Mendelian, Complex and Environmental Diseases

PubMed Central

Bauer-Mehren, Anna; Bundschus, Markus; Rautschka, Michael; Mayer, Miguel A.; Sanz, Ferran; Furlong, Laura I.

2011-01-01

Background Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. Principal Findings We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. Conclusions For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. Availability The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download. PMID:21695124

Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases.

PubMed

Bauer-Mehren, Anna; Bundschus, Markus; Rautschka, Michael; Mayer, Miguel A; Sanz, Ferran; Furlong, Laura I

2011-01-01

Scientists have been trying to understand the molecular mechanisms of diseases to design preventive and therapeutic strategies for a long time. For some diseases, it has become evident that it is not enough to obtain a catalogue of the disease-related genes but to uncover how disruptions of molecular networks in the cell give rise to disease phenotypes. Moreover, with the unprecedented wealth of information available, even obtaining such catalogue is extremely difficult. We developed a comprehensive gene-disease association database by integrating associations from several sources that cover different biomedical aspects of diseases. In particular, we focus on the current knowledge of human genetic diseases including mendelian, complex and environmental diseases. To assess the concept of modularity of human diseases, we performed a systematic study of the emergent properties of human gene-disease networks by means of network topology and functional annotation analysis. The results indicate a highly shared genetic origin of human diseases and show that for most diseases, including mendelian, complex and environmental diseases, functional modules exist. Moreover, a core set of biological pathways is found to be associated with most human diseases. We obtained similar results when studying clusters of diseases, suggesting that related diseases might arise due to dysfunction of common biological processes in the cell. For the first time, we include mendelian, complex and environmental diseases in an integrated gene-disease association database and show that the concept of modularity applies for all of them. We furthermore provide a functional analysis of disease-related modules providing important new biological insights, which might not be discovered when considering each of the gene-disease association repositories independently. Hence, we present a suitable framework for the study of how genetic and environmental factors, such as drugs, contribute to diseases. The gene-disease networks used in this study and part of the analysis are available at http://ibi.imim.es/DisGeNET/DisGeNETweb.html#Download.

Launch Control Network Engineer

NASA Technical Reports Server (NTRS)

Medeiros, Samantha

2017-01-01

The Spaceport Command and Control System (SCCS) is being built at the Kennedy Space Center in order to successfully launch NASA’s revolutionary vehicle that allows humans to explore further into space than ever before. During my internship, I worked with the Network, Firewall, and Hardware teams that are all contributing to the huge SCCS network project effort. I learned the SCCS network design and the several concepts that are running in the background. I also updated and designed documentation for physical networks that are part of SCCS. This includes being able to assist and build physical installations as well as configurations. I worked with the network design for vehicle telemetry interfaces to the Launch Control System (LCS); this allows the interface to interact with other systems at other NASA locations. This network design includes the Space Launch System (SLS), Interim Cryogenic Propulsion Stage (ICPS), and the Orion Multipurpose Crew Vehicle (MPCV). I worked on the network design and implementation in the Customer Avionics Interface Development and Analysis (CAIDA) lab.

Concepts to Support HRP Integration Using Publications and Modeling

NASA Technical Reports Server (NTRS)

Mindock, J.; Lumpkins, S.; Shelhamer, M.

2014-01-01

Initial efforts are underway to enhance the Human Research Program (HRP)'s identification and support of potential cross-disciplinary scientific collaborations. To increase the emphasis on integration in HRP's science portfolio management, concepts are being explored through the development of a set of tools. These tools are intended to enable modeling, analysis, and visualization of the state of the human system in the spaceflight environment; HRP's current understanding of that state with an indication of uncertainties; and how that state changes due to HRP programmatic progress and design reference mission definitions. In this talk, we will discuss proof-of-concept work performed using a subset of publications captured in the HRP publications database. The publications were tagged in the database with words representing factors influencing health and performance in spaceflight, as well as with words representing the risks HRP research is reducing. Analysis was performed on the publication tag data to identify relationships between factors and between risks. Network representations were then created as one type of visualization of these relationships. This enables future analyses of the structure of the networks based on results from network theory. Such analyses can provide insights into HRP's current human system knowledge state as informed by the publication data. The network structure analyses can also elucidate potential improvements by identifying network connections to establish or strengthen for maximized information flow. The relationships identified in the publication data were subsequently used as inputs to a model captured in the Systems Modeling Language (SysML), which functions as a repository for relationship information to be gleaned from multiple sources. Example network visualization outputs from a simple SysML model were then also created to compare to the visualizations based on the publication data only. We will also discuss ideas for building upon this proof-of-concept work to further support an integrated approach to human spaceflight risk reduction.

Cloud-based robot remote control system for smart factory

NASA Astrophysics Data System (ADS)

Wu, Zhiming; Li, Lianzhong; Xu, Yang; Zhai, Jingmei

2015-12-01

With the development of internet technologies and the wide application of robots, there is a prospect (trend/tendency) of integration between network and robots. A cloud-based robot remote control system over networks for smart factory is proposed, which enables remote users to control robots and then realize intelligent production. To achieve it, a three-layer system architecture is designed including user layer, service layer and physical layer. Remote control applications running on the cloud server is developed on Microsoft Azure. Moreover, DIV+ CSS technologies are used to design human-machine interface to lower maintenance cost and improve development efficiency. Finally, an experiment is implemented to verify the feasibility of the program.

Associating Human-Centered Concepts with Social Networks Using Fuzzy Sets

NASA Astrophysics Data System (ADS)

Yager, Ronald R.

The rapidly growing global interconnectivity, brought about to a large extent by the Internet, has dramatically increased the importance and diversity of social networks. Modern social networks cut across a spectrum from benign recreational focused websites such as Facebook to occupationally oriented websites such as LinkedIn to criminally focused groups such as drug cartels to devastation and terror focused groups such as Al-Qaeda. Many organizations are interested in analyzing and extracting information related to these social networks. Among these are governmental police and security agencies as well marketing and sales organizations. To aid these organizations there is a need for technologies to model social networks and intelligently extract information from these models. While established technologies exist for the modeling of relational networks [1-7] few technologies exist to extract information from these, compatible with human perception and understanding. Data bases is an example of a technology in which we have tools for representing our information as well as tools for querying and extracting the information contained. Our goal is in some sense analogous. We want to use the relational network model to represent information, in this case about relationships and interconnections, and then be able to query the social network using intelligent human-centered concepts. To extend our capabilities to interact with social relational networks we need to associate with these network human concepts and ideas. Since human beings predominantly use linguistic terms in which to reason and understand we need to build bridges between human conceptualization and the formal mathematical representation of the social network. Consider for example a concept such as "leader". An analyst may be able to express, in linguistic terms, using a network relevant vocabulary, properties of a leader. Our task is to translate this linguistic description into a mathematical formalism that allows us to determine how true it is that a particular node is a leader. In this work we look at the use of fuzzy set methodologies [8-10] to provide a bridge between the human analyst and the formal model of the network.

The Network Structure of Human Personality According to the NEO-PI-R: Matching Network Community Structure to Factor Structure

PubMed Central

Goekoop, Rutger; Goekoop, Jaap G.; Scholte, H. Steven

2012-01-01

Introduction Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. Aim To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). Methods 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. Results At facet level, NCS showed a best match (96.2%) with a ‘confirmatory’ 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with ‘confirmatory’ 5-FS and ‘exploratory’ 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. Conclusion We present the first optimized network graph of personality traits according to the NEO-PI-R: a ‘Personality Web’. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network. PMID:23284713

The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

PubMed

Goekoop, Rutger; Goekoop, Jaap G; Scholte, H Steven

2012-01-01

Human personality is described preferentially in terms of factors (dimensions) found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. To directly compare the ability of network community detection (NCD) and principal component factor analysis (PCA) to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R). 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS) of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS) of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. At facet level, NCS showed a best match (96.2%) with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80%) with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

Brain Consequences of Spinal Cord Injury with and without Neuropathic Pain: Translating Animal Models of Neuroinflammation onto Human Neural Networks and Back

DTIC Science & Technology

2016-10-01

During year one , we have: Obtained IRB and HRPO approval for the human studies , obtained IACUC and ACURO approval for the animal studies , refined the...human study protocol and collected PET-MR data on healthy individuals and spinal cord injured subjects, developed the rodent imaging procedures...qualtiative synthesis of the current state of the field, and 6 studies can be included in a quantitative meta-analysis. The studies eligible for inclusion in

Functional evolution of new and expanded attention networks in humans

PubMed Central

Patel, Gaurav H.; Yang, Danica; Jamerson, Emery C.; Snyder, Lawrence H.; Corbetta, Maurizio; Ferrera, Vincent P.

2015-01-01

Macaques are often used as a model system for invasive investigations of the neural substrates of cognition. However, 25 million years of evolution separate humans and macaques from their last common ancestor, and this has likely substantially impacted the function of the cortical networks underlying cognitive processes, such as attention. We examined the homology of frontoparietal networks underlying attention by comparing functional MRI data from macaques and humans performing the same visual search task. Although there are broad similarities, we found fundamental differences between the species. First, humans have more dorsal attention network areas than macaques, indicating that in the course of evolution the human attention system has expanded compared with macaques. Second, potentially homologous areas in the dorsal attention network have markedly different biases toward representing the contralateral hemifield, indicating that the underlying neural architecture of these areas may differ in the most basic of properties, such as receptive field distribution. Third, despite clear evidence of the temporoparietal junction node of the ventral attention network in humans as elicited by this visual search task, we did not find functional evidence of a temporoparietal junction in macaques. None of these differences were the result of differences in training, experimental power, or anatomical variability between the two species. The results of this study indicate that macaque data should be applied to human models of cognition cautiously, and demonstrate how evolution may shape cortical networks. PMID:26170314

Functional evolution of new and expanded attention networks in humans.

PubMed

Patel, Gaurav H; Yang, Danica; Jamerson, Emery C; Snyder, Lawrence H; Corbetta, Maurizio; Ferrera, Vincent P

2015-07-28

Macaques are often used as a model system for invasive investigations of the neural substrates of cognition. However, 25 million years of evolution separate humans and macaques from their last common ancestor, and this has likely substantially impacted the function of the cortical networks underlying cognitive processes, such as attention. We examined the homology of frontoparietal networks underlying attention by comparing functional MRI data from macaques and humans performing the same visual search task. Although there are broad similarities, we found fundamental differences between the species. First, humans have more dorsal attention network areas than macaques, indicating that in the course of evolution the human attention system has expanded compared with macaques. Second, potentially homologous areas in the dorsal attention network have markedly different biases toward representing the contralateral hemifield, indicating that the underlying neural architecture of these areas may differ in the most basic of properties, such as receptive field distribution. Third, despite clear evidence of the temporoparietal junction node of the ventral attention network in humans as elicited by this visual search task, we did not find functional evidence of a temporoparietal junction in macaques. None of these differences were the result of differences in training, experimental power, or anatomical variability between the two species. The results of this study indicate that macaque data should be applied to human models of cognition cautiously, and demonstrate how evolution may shape cortical networks.

Effect of Prevascularization on In Vivo Vascularization of Poly(Propylene fumarate)/Fibrin Scaffolds

PubMed Central

Mishra, Ruchi; Roux, Brianna M.; Posukonis, Megan; Bodamer, Emily; Brey, Eric M.; Fisher, John P.; Dean, David

2016-01-01

The importance of vascularization in the field of bone tissue engineering has been established by previous studies. The present work proposes a novel poly(propylene fumarate) (PPF)/fibrin composite scaffold for the development of vascularized neobone tissue. The effect of prevascularization (i.e., in vitro pre-culture prior to implantation) with human mesenchymal stem cells (hMSCs) and human umbilical vein endothelial cells (HUVECs) on in vivo vascularization of scaffolds was determined. Five conditions were studied: no pre-culture (NP), 1 week preculture (1P), 2 week pre-culture (2P), 3 week pre-culture (3P), and scaffolds without cells (control, C). Scaffolds were implanted subcutaneously in a severe combined immunodeficiency (SCID) mice model for 9 days. During in vitro studies, CD31 staining showed a significant increase in vascular network area over 3 weeks of culture. Vascular density was significantly higher in vivo when comparing NP to 3P groups. Immunohistochemical staining of human CD-31 expression indicated spreading of vascular networks with increasing pre-culture time. These vascular networks were perfused with mouse blood indicated by perfused lectin staining in human CD-31 positive vessels. Our results demonstrate that in vitro prevascularization supports in vivo vascularization in PPF/fibrin scaffolds. PMID:26606451

COST action TD1407: network on technology-critical elements (NOTICE)--from environmental processes to human health threats.

PubMed

Cobelo-García, A; Filella, M; Croot, P; Frazzoli, C; Du Laing, G; Ospina-Alvarez, N; Rauch, S; Salaun, P; Schäfer, J; Zimmermann, S

2015-10-01

The current socio-economic, environmental and public health challenges that countries are facing clearly need common-defined strategies to inform and support our transition to a sustainable economy. Here, the technology-critical elements (which includes Ga, Ge, In, Te, Nb, Ta, Tl, the Platinum Group Elements and most of the rare-earth elements) are of great relevance in the development of emerging key technologies-including renewable energy, energy efficiency, electronics or the aerospace industry. In this context, the increasing use of technology-critical elements (TCEs) and associated environmental impacts (from mining to end-of-life waste products) is not restricted to a national level but covers most likely a global scale. Accordingly, the European COST Action TD1407: Network on Technology-Critical Elements (NOTICE)-from environmental processes to human health threats, has an overall objective for creating a network of scientists and practitioners interested in TCEs, from the evaluation of their environmental processes to understanding potential human health threats, with the aim of defining the current state of knowledge and gaps, proposing priority research lines/activities and acting as a platform for new collaborations and joint research projects. The Action is focused on three major scientific areas: (i) analytical chemistry, (ii) environmental biogeochemistry and (iii) human exposure and (eco)-toxicology.

Observational Needs for Four-Dimensional Air Quality Characterization

EPA Science Inventory

Surface-based monitoring programs provide the foundation for associating air pollution and causal effects in human health studies, and they support the development of air quality standards and the preparation of emission reduction strategies. While surface oriented networks remai...

fastBMA: scalable network inference and transitive reduction.

PubMed

Hung, Ling-Hong; Shi, Kaiyuan; Wu, Migao; Young, William Chad; Raftery, Adrian E; Yeung, Ka Yee

2017-10-01

Inferring genetic networks from genome-wide expression data is extremely demanding computationally. We have developed fastBMA, a distributed, parallel, and scalable implementation of Bayesian model averaging (BMA) for this purpose. fastBMA also includes a computationally efficient module for eliminating redundant indirect edges in the network by mapping the transitive reduction to an easily solved shortest-path problem. We evaluated the performance of fastBMA on synthetic data and experimental genome-wide time series yeast and human datasets. When using a single CPU core, fastBMA is up to 100 times faster than the next fastest method, LASSO, with increased accuracy. It is a memory-efficient, parallel, and distributed application that scales to human genome-wide expression data. A 10 000-gene regulation network can be obtained in a matter of hours using a 32-core cloud cluster (2 nodes of 16 cores). fastBMA is a significant improvement over its predecessor ScanBMA. It is more accurate and orders of magnitude faster than other fast network inference methods such as the 1 based on LASSO. The improved scalability allows it to calculate networks from genome scale data in a reasonable time frame. The transitive reduction method can improve accuracy in denser networks. fastBMA is available as code (M.I.T. license) from GitHub (https://github.com/lhhunghimself/fastBMA), as part of the updated networkBMA Bioconductor package (https://www.bioconductor.org/packages/release/bioc/html/networkBMA.html) and as ready-to-deploy Docker images (https://hub.docker.com/r/biodepot/fastbma/). © The Authors 2017. Published by Oxford University Press.

The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations.

PubMed

Keenan, Alexandra B; Jenkins, Sherry L; Jagodnik, Kathleen M; Koplev, Simon; He, Edward; Torre, Denis; Wang, Zichen; Dohlman, Anders B; Silverstein, Moshe C; Lachmann, Alexander; Kuleshov, Maxim V; Ma'ayan, Avi; Stathias, Vasileios; Terryn, Raymond; Cooper, Daniel; Forlin, Michele; Koleti, Amar; Vidovic, Dusica; Chung, Caty; Schürer, Stephan C; Vasiliauskas, Jouzas; Pilarczyk, Marcin; Shamsaei, Behrouz; Fazel, Mehdi; Ren, Yan; Niu, Wen; Clark, Nicholas A; White, Shana; Mahi, Naim; Zhang, Lixia; Kouril, Michal; Reichard, John F; Sivaganesan, Siva; Medvedovic, Mario; Meller, Jaroslaw; Koch, Rick J; Birtwistle, Marc R; Iyengar, Ravi; Sobie, Eric A; Azeloglu, Evren U; Kaye, Julia; Osterloh, Jeannette; Haston, Kelly; Kalra, Jaslin; Finkbiener, Steve; Li, Jonathan; Milani, Pamela; Adam, Miriam; Escalante-Chong, Renan; Sachs, Karen; Lenail, Alex; Ramamoorthy, Divya; Fraenkel, Ernest; Daigle, Gavin; Hussain, Uzma; Coye, Alyssa; Rothstein, Jeffrey; Sareen, Dhruv; Ornelas, Loren; Banuelos, Maria; Mandefro, Berhan; Ho, Ritchie; Svendsen, Clive N; Lim, Ryan G; Stocksdale, Jennifer; Casale, Malcolm S; Thompson, Terri G; Wu, Jie; Thompson, Leslie M; Dardov, Victoria; Venkatraman, Vidya; Matlock, Andrea; Van Eyk, Jennifer E; Jaffe, Jacob D; Papanastasiou, Malvina; Subramanian, Aravind; Golub, Todd R; Erickson, Sean D; Fallahi-Sichani, Mohammad; Hafner, Marc; Gray, Nathanael S; Lin, Jia-Ren; Mills, Caitlin E; Muhlich, Jeremy L; Niepel, Mario; Shamu, Caroline E; Williams, Elizabeth H; Wrobel, David; Sorger, Peter K; Heiser, Laura M; Gray, Joe W; Korkola, James E; Mills, Gordon B; LaBarge, Mark; Feiler, Heidi S; Dane, Mark A; Bucher, Elmar; Nederlof, Michel; Sudar, Damir; Gross, Sean; Kilburn, David F; Smith, Rebecca; Devlin, Kaylyn; Margolis, Ron; Derr, Leslie; Lee, Albert; Pillai, Ajay

2018-01-24

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability. Copyright © 2017 Elsevier Inc. All rights reserved.

FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch

PubMed Central

Day, Nancy F; Kimball, Todd Haswell; Aamodt, Caitlin M; Heston, Jonathan B; Hilliard, Austin T; Xiao, Xinshu; White, Stephanie A

2018-01-01

Human speech is one of the few examples of vocal learning among mammals yet ~half of avian species exhibit this ability. Its neurogenetic basis is largely unknown beyond a shared requirement for FoxP2 in both humans and zebra finches. We manipulated FoxP2 isoforms in Area X, a song-specific region of the avian striatopallidum analogous to human anterior striatum, during a critical period for song development. We delineate, for the first time, unique contributions of each isoform to vocal learning. Weighted gene coexpression network analysis of RNA-seq data revealed gene modules correlated to singing, learning, or vocal variability. Coexpression related to singing was found in juvenile and adult Area X whereas coexpression correlated to learning was unique to juveniles. The confluence of learning and singing coexpression in juvenile Area X may underscore molecular processes that drive vocal learning in young zebra finches and, by analogy, humans. PMID:29360038

Applications of neural networks to landmark detection in 3-D surface data

NASA Astrophysics Data System (ADS)

Arndt, Craig M.

1992-09-01

The problem of identifying key landmarks in 3-dimensional surface data is of considerable interest in solving a number of difficult real-world tasks, including object recognition and image processing. The specific problem that we address in this research is to identify the specific landmarks (anatomical) in human surface data. This is a complex task, currently performed visually by an expert human operator. In order to replace these human operators and increase reliability of the data acquisition, we need to develop a computer algorithm which will utilize the interrelations between the 3-dimensional data to identify the landmarks of interest. The current presentation describes a method for designing, implementing, training, and testing a custom architecture neural network which will perform the landmark identification task. We discuss the performance of the net in relationship to human performance on the same task and how this net has been integrated with other AI and traditional programming methods to produce a powerful analysis tool for computer anthropometry.

Detection of type 2 diabetes related modules and genes based on epigenetic networks

PubMed Central

2014-01-01

Background Type 2 diabetes (T2D) is one of the most common chronic metabolic diseases characterized by insulin resistance and the decrease of insulin secretion. Genetic variation can only explain part of the heritability of T2D, so there need new methods to detect the susceptibility genes of the disease. Epigenetics could establish the interface between the environmental factor and the T2D Pathological mechanism. Results Based on the network theory and by combining epigenetic characteristics with human interactome, the weighted human DNA methylation network (WMPN) was constructed, and a T2D-related subnetwork (TMSN) was obtained through T2D-related differentially methylated genes. It is found that TMSN had a T2D specific network structure that non-fatal metabolic disease causing genes were often located in the topological and functional periphery of network. Combined with chromatin modifications, the weighted chromatin modification network (WCPN) was built, and a T2D-related chromatin modification pattern subnetwork was obtained by the TMSN gene set. TCSN had a densely connected network community, indicating that TMSN and TCSN could represent a collection of T2D-related epigenetic dysregulated sub-pathways. Using the cumulative hypergeometric test, 24 interplay modules of DNA methylation and chromatin modifications were identified. By the analysis of gene expression in human T2D islet tissue, it is found that there existed genes with the variant expression level caused by the aberrant DNA methylation and (or) chromatin modifications, which might affect and promote the development of T2D. Conclusions Here we have detected the potential interplay modules of DNA methylation and chromatin modifications for T2D. The study of T2D epigenetic networks provides a new way for understanding the pathogenic mechanism of T2D caused by epigenetic disorders. PMID:24565181

The Internet and the Humanities: The Human Side of Networking. The Future of Networking Technologies for Learning.

ERIC Educational Resources Information Center

Riel, Margaret

This paper focuses on the human side of networking in education, emphasizing that people working together can create new solutions to old problems. Discussion includes increased links to information through the Internet and new resources entering the classroom; team-teaching, cooperative learning, and learner-centered instruction; and distance…

Current Research in European Vocational Education and Human Resource Development. Proceedings of the Programme Presented By the Research Network on Vocational Education and Training (VETNET) at the European Conference of Educational Research (ECER) (3rd, Edinburgh, Scotland, September 20-23, 2000).

ERIC Educational Resources Information Center

Manning, Sabine, Ed.; Raffe, David, Ed.

These 24 papers represent the proceedings of a program presented by the research network on vocational education and training (VET). They include "School-Arranged or Market-Governed Workplace Training?" (Ulla Arnell-Gustafsson); "Prospects for Mutual Learning and Transnational Transfer of Innovative Practice in European VET"…

Neural Network Classifies Teleoperation Data

NASA Technical Reports Server (NTRS)

Fiorini, Paolo; Giancaspro, Antonio; Losito, Sergio; Pasquariello, Guido

1994-01-01

Prototype artificial neural network, implemented in software, identifies phases of telemanipulator tasks in real time by analyzing feedback signals from force sensors on manipulator hand. Prototype is early, subsystem-level product of continuing effort to develop automated system that assists in training and supervising human control operator: provides symbolic feedback (e.g., warnings of impending collisions or evaluations of performance) to operator in real time during successive executions of same task. Also simplifies transition between teleoperation and autonomous modes of telerobotic system.

Infrastructure resources for clinical research in amyotrophic lateral sclerosis.

PubMed

Sherman, Alexander V; Gubitz, Amelie K; Al-Chalabi, Ammar; Bedlack, Richard; Berry, James; Conwit, Robin; Harris, Brent T; Horton, D Kevin; Kaufmann, Petra; Leitner, Melanie L; Miller, Robert; Shefner, Jeremy; Vonsattel, Jean Paul; Mitsumoto, Hiroshi

2013-05-01

Clinical trial networks, shared clinical databases, and human biospecimen repositories are examples of infrastructure resources aimed at enhancing and expediting clinical and/or patient oriented research to uncover the etiology and pathogenesis of amyotrophic lateral sclerosis (ALS), a rapidly progressive neurodegenerative disease that leads to the paralysis of voluntary muscles. The current status of such infrastructure resources, as well as opportunities and impediments, were discussed at the second Tarrytown ALS meeting held in September 2011. The discussion focused on resources developed and maintained by ALS clinics and centers in North America and Europe, various clinical trial networks, U.S. government federal agencies including the National Institutes of Health (NIH), the Agency for Toxic Substances and Disease Registry (ATSDR) and the Centers for Disease Control and Prevention (CDC), and several voluntary disease organizations that support ALS research activities. Key recommendations included 1) the establishment of shared databases among individual ALS clinics to enhance the coordination of resources and data analyses; 2) the expansion of quality-controlled human biospecimen banks; and 3) the adoption of uniform data standards, such as the recently developed Common Data Elements (CDEs) for ALS clinical research. The value of clinical trial networks such as the Northeast ALS (NEALS) Consortium and the Western ALS (WALS) Consortium was recognized, and strategies to further enhance and complement these networks and their research resources were discussed.

Report on the 6th African Society of Human Genetics (AfSHG) Meeting, March 12-15, 2009, Yaounde, Cameroon.

PubMed

Sirugo, Giorgio; Williams, Scott M; Royal, Charmaine D M; Newport, Melanie J; Hennig, Branwen J; Mariani-Costantini, Renato; Buonaguro, Franco M; Velez Edwards, Digna R; Ibrahim, Muntaser; Soodyall, Himla; Wonkam, Ambroise; Ramesar, Raj; Rotimi, Charles N

2010-08-01

The African Society of Human Genetics (AfSHG), founded in 2003 with its inaugural meeting in Accra, Ghana,1 has the stated missions of (1) disseminating information about human genetics research in Africa, (2) establishing a mentorship network providing educational resources, including the development of appropriate technology transfer, (3) providing advocacy for human genetic research in Africa, and (4) encouraging collaborative research. Despite its young age, the AfSHG has developed a strong cadre of active researchers, both within and outside of Africa, with more than 400 members (from 16 countries across Africa as well as 8 other countries), and has held six successful meetings, five in Africa and one in the United States.

Hopfield networks for solving Tower of Hanoi problems

NASA Astrophysics Data System (ADS)

Kaplan, G. B.; Güzeliş, Cüneyt

2001-08-01

In this paper, Hopfield neural networks have been considered in solving the Tower of Hanoi test which is used in the determining of deficit of planning capability of the human prefrontal cortex. The main difference between this paper and the ones in the literature which use neural networks is that the Tower of Hanoi problem has been formulated here as a special shortest-path problem. In the literature, some Hopfield networks are developed for solving the shortest path problem which is a combinatorial optimization problem having a diverse field of application. The approach given in this paper gives the possibility of solving the Tower of Hanoi problem using these Hopfield networks. Also, the paper proposes new Hopfield network models for the shortest path and hence the Tower of Hanoi problems and compares them to the available ones in terms of the memory and time (number of steps) needed in the simulations.

Multiple tipping points and optimal repairing in interacting networks

PubMed Central

Majdandzic, Antonio; Braunstein, Lidia A.; Curme, Chester; Vodenska, Irena; Levy-Carciente, Sary; Eugene Stanley, H.; Havlin, Shlomo

2016-01-01

Systems composed of many interacting dynamical networks—such as the human body with its biological networks or the global economic network consisting of regional clusters—often exhibit complicated collective dynamics. Three fundamental processes that are typically present are failure, damage spread and recovery. Here we develop a model for such systems and find a very rich phase diagram that becomes increasingly more complex as the number of interacting networks increases. In the simplest example of two interacting networks we find two critical points, four triple points, ten allowed transitions and two ‘forbidden' transitions, as well as complex hysteresis loops. Remarkably, we find that triple points play the dominant role in constructing the optimal repairing strategy in damaged interacting systems. To test our model, we analyse an example of real interacting financial networks and find evidence of rapid dynamical transitions between well-defined states, in agreement with the predictions of our model. PMID:26926803

The development of hub architecture in the human functional brain network.

PubMed

Hwang, Kai; Hallquist, Michael N; Luna, Beatriz

2013-10-01

Functional hubs are brain regions that play a crucial role in facilitating communication among parallel, distributed brain networks. The developmental emergence and stability of hubs, however, is not well understood. The current study used measures of network topology drawn from graph theory to investigate the development of functional hubs in 99 participants, 10-20 years of age. We found that hub architecture was evident in late childhood and was stable from adolescence to early adulthood. Connectivity between hub and non-hub ("spoke") regions, however, changed with development. From childhood to adolescence, the strength of connections between frontal hubs and cortical and subcortical spoke regions increased. From adolescence to adulthood, hub-spoke connections with frontal hubs were stable, whereas connectivity between cerebellar hubs and cortical spoke regions increased. Our findings suggest that a developmentally stable functional hub architecture provides the foundation of information flow in the brain, whereas connections between hubs and spokes continue to develop, possibly supporting mature cognitive function.

Linguistic complex networks: Rationale, application, interpretation, and directions. Reply to comments on "Approaching human language with complex networks"

NASA Astrophysics Data System (ADS)

Cong, Jin; Liu, Haitao

2014-12-01

Amid the enthusiasm for real-world networks of the new millennium, the enquiry into linguistic networks is flourishing not only as a productive branch of the new networks science but also as a promising approach to linguistic research. Although the complex network approach constitutes a potential opportunity to make linguistics a science, the world of linguistics seems unprepared to embrace it. For one thing, linguistics has been largely unaffected by quantitative methods. Those who are accustomed to qualitative linguistic methods may find it hard to appreciate the application of quantitative properties of language such as frequency and length, not to mention quantitative properties of language modeled as networks. With this in mind, in our review [1] we restrict ourselves to the basics of complex networks and the new insights into human language with the application of complex networks. For another, while breaking new grounds and posing new challenges for linguistics, the complex network approach to human language as a new tradition of linguistic research is faced with challenges and unsolved issues of its own. It is no surprise that the comments on our review, especially their skepticism and suggestions, focus on various different aspects of the complex network approach to human language. We are grateful to all the insightful and penetrating comments, which, together with our review, mark a significant impetus to linguistic research from the complex network approach. In this reply, we would like to address four major issues of the complex network approach to human language, namely, a) its theoretical rationale, b) its application in linguistic research, c) interpretation of the results, and d) directions of future research.

Studying the Genetics of Complex Disease With Ancestry‐Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations

PubMed Central

Qiu, Jingya; Darabos, Christian

2016-01-01

ABSTRACT Genome‐wide association studies (GWAS) have led to the discovery of over 200 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes mellitus (T2DM). Additionally, East Asians develop T2DM at a higher rate, younger age, and lower body mass index than their European ancestry counterparts. The reason behind this occurrence remains elusive. With comprehensive searches through the National Human Genome Research Institute (NHGRI) GWAS catalog literature, we compiled a database of 2,800 ancestry‐specific SNPs associated with T2DM and 70 other related traits. Manual data extraction was necessary because the GWAS catalog reports statistics such as odds ratio and P‐value, but does not consistently include ancestry information. Currently, many statistics are derived by combining initial and replication samples from study populations of mixed ancestry. Analysis of all‐inclusive data can be misleading, as not all SNPs are transferable across diverse populations. We used ancestry data to construct ancestry‐specific human phenotype networks (HPN) centered on T2DM. Quantitative and visual analysis of network models reveal the genetic disparities between ancestry groups. Of the 27 phenotypes in the East Asian HPN, six phenotypes were unique to the network, revealing the underlying ancestry‐specific nature of some SNPs associated with T2DM. We studied the relationship between T2DM and five phenotypes unique to the East Asian HPN to generate new interaction hypotheses in a clinical context. The genetic differences found in our ancestry‐specific HPNs suggest different pathways are involved in the pathogenesis of T2DM among different populations. Our study underlines the importance of ancestry in the development of T2DM and its implications in pharmocogenetics and personalized medicine. PMID:27061195

Structural health monitoring for bolt loosening via a non-invasive vibro-haptics human-machine cooperative interface

NASA Astrophysics Data System (ADS)

Pekedis, Mahmut; Mascerañas, David; Turan, Gursoy; Ercan, Emre; Farrar, Charles R.; Yildiz, Hasan

2015-08-01

For the last two decades, developments in damage detection algorithms have greatly increased the potential for autonomous decisions about structural health. However, we are still struggling to build autonomous tools that can match the ability of a human to detect and localize the quantity of damage in structures. Therefore, there is a growing interest in merging the computational and cognitive concepts to improve the solution of structural health monitoring (SHM). The main object of this research is to apply the human-machine cooperative approach on a tower structure to detect damage. The cooperation approach includes haptic tools to create an appropriate collaboration between SHM sensor networks, statistical compression techniques and humans. Damage simulation in the structure is conducted by releasing some of the bolt loads. Accelerometers are bonded to various locations of the tower members to acquire the dynamic response of the structure. The obtained accelerometer results are encoded in three different ways to represent them as a haptic stimulus for the human subjects. Then, the participants are subjected to each of these stimuli to detect the bolt loosened damage in the tower. Results obtained from the human-machine cooperation demonstrate that the human subjects were able to recognize the damage with an accuracy of 88 ± 20.21% and response time of 5.87 ± 2.33 s. As a result, it is concluded that the currently developed human-machine cooperation SHM may provide a useful framework to interact with abstract entities such as data from a sensor network.

Characterizing and Measuring Maliciousness for Cybersecurity Risk Assessment

PubMed Central

King, Zoe M.; Henshel, Diane S.; Flora, Liberty; Cains, Mariana G.; Hoffman, Blaine; Sample, Char

2018-01-01

Cyber attacks have been increasingly detrimental to networks, systems, and users, and are increasing in number and severity globally. To better predict system vulnerabilities, cybersecurity researchers are developing new and more holistic approaches to characterizing cybersecurity system risk. The process must include characterizing the human factors that contribute to cyber security vulnerabilities and risk. Rationality, expertise, and maliciousness are key human characteristics influencing cyber risk within this context, yet maliciousness is poorly characterized in the literature. There is a clear absence of literature pertaining to human factor maliciousness as it relates to cybersecurity and only limited literature relating to aspects of maliciousness in other disciplinary literatures, such as psychology, sociology, and law. In an attempt to characterize human factors as a contribution to cybersecurity risk, the Cybersecurity Collaborative Research Alliance (CSec-CRA) has developed a Human Factors risk framework. This framework identifies the characteristics of an attacker, user, or defender, all of whom may be adding to or mitigating against cyber risk. The maliciousness literature and the proposed maliciousness assessment metrics are discussed within the context of the Human Factors Framework and Ontology. Maliciousness is defined as the intent to harm. Most maliciousness cyber research to date has focused on detecting malicious software but fails to analyze an individual’s intent to do harm to others by deploying malware or performing malicious attacks. Recent efforts to identify malicious human behavior as it relates to cybersecurity, include analyzing motives driving insider threats as well as user profiling analyses. However, cyber-related maliciousness is neither well-studied nor is it well understood because individuals are not forced to expose their true selves to others while performing malicious attacks. Given the difficulty of interviewing malicious-behaving individuals and the potential untrustworthy nature of their responses, we aim to explore the maliciousness as a human factor through the observable behaviors and attributes of an individual from their actions and interactions with society and networks, but to do so we will need to develop a set of analyzable metrics. The purpose of this paper is twofold: (1) to review human maliciousness-related literature in diverse disciplines (sociology, economics, law, psychology, philosophy, informatics, terrorism, and cybersecurity); and (2) to identify an initial set of proposed assessment metrics and instruments that might be culled from in a future effort to characterize human maliciousness within the cyber realm. The future goal is to integrate these assessment metrics into holistic cybersecurity risk analyses to determine the risk an individual poses to themselves as well as other networks, systems, and/or users. PMID:29459838

Characterizing and Measuring Maliciousness for Cybersecurity Risk Assessment.

PubMed

King, Zoe M; Henshel, Diane S; Flora, Liberty; Cains, Mariana G; Hoffman, Blaine; Sample, Char

2018-01-01

Cyber attacks have been increasingly detrimental to networks, systems, and users, and are increasing in number and severity globally. To better predict system vulnerabilities, cybersecurity researchers are developing new and more holistic approaches to characterizing cybersecurity system risk. The process must include characterizing the human factors that contribute to cyber security vulnerabilities and risk. Rationality, expertise, and maliciousness are key human characteristics influencing cyber risk within this context, yet maliciousness is poorly characterized in the literature. There is a clear absence of literature pertaining to human factor maliciousness as it relates to cybersecurity and only limited literature relating to aspects of maliciousness in other disciplinary literatures, such as psychology, sociology, and law. In an attempt to characterize human factors as a contribution to cybersecurity risk, the Cybersecurity Collaborative Research Alliance (CSec-CRA) has developed a Human Factors risk framework. This framework identifies the characteristics of an attacker, user, or defender, all of whom may be adding to or mitigating against cyber risk. The maliciousness literature and the proposed maliciousness assessment metrics are discussed within the context of the Human Factors Framework and Ontology. Maliciousness is defined as the intent to harm. Most maliciousness cyber research to date has focused on detecting malicious software but fails to analyze an individual's intent to do harm to others by deploying malware or performing malicious attacks. Recent efforts to identify malicious human behavior as it relates to cybersecurity, include analyzing motives driving insider threats as well as user profiling analyses. However, cyber-related maliciousness is neither well-studied nor is it well understood because individuals are not forced to expose their true selves to others while performing malicious attacks. Given the difficulty of interviewing malicious-behaving individuals and the potential untrustworthy nature of their responses, we aim to explore the maliciousness as a human factor through the observable behaviors and attributes of an individual from their actions and interactions with society and networks, but to do so we will need to develop a set of analyzable metrics. The purpose of this paper is twofold: (1) to review human maliciousness-related literature in diverse disciplines (sociology, economics, law, psychology, philosophy, informatics, terrorism, and cybersecurity); and (2) to identify an initial set of proposed assessment metrics and instruments that might be culled from in a future effort to characterize human maliciousness within the cyber realm. The future goal is to integrate these assessment metrics into holistic cybersecurity risk analyses to determine the risk an individual poses to themselves as well as other networks, systems, and/or users.

Computing smallest intervention strategies for multiple metabolic networks in a boolean model.

PubMed

Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya

2015-02-01

This article considers the problem whereby, given two metabolic networks N1 and N2, a set of source compounds, and a set of target compounds, we must find the minimum set of reactions whose removal (knockout) ensures that the target compounds are not producible in N1 but are producible in N2. Similar studies exist for the problem of finding the minimum knockout with the smallest side effect for a single network. However, if technologies of external perturbations are advanced in the near future, it may be important to develop methods of computing the minimum knockout for multiple networks (MKMN). Flux balance analysis (FBA) is efficient if a well-polished model is available. However, that is not always the case. Therefore, in this article, we study MKMN in Boolean models and an elementary mode (EM)-based model. Integer linear programming (ILP)-based methods are developed for these models, since MKMN is NP-complete for both the Boolean model and the EM-based model. Computer experiments are conducted with metabolic networks of clostridium perfringens SM101 and bifidobacterium longum DJO10A, respectively known as bad bacteria and good bacteria for the human intestine. The results show that larger networks are more likely to have MKMN solutions. However, solving for these larger networks takes a very long time, and often the computation cannot be completed. This is reasonable, because small networks do not have many alternative pathways, making it difficult to satisfy the MKMN condition, whereas in large networks the number of candidate solutions explodes. Our developed software minFvskO is available online.

[-25]A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks.

PubMed

García-Hernández, Alejandra; Galván-Tejada, Carlos E; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Velasco-Elizondo, Perla; Cárdenas-Vargas, Rogelio

2017-11-21

Human Activity Recognition (HAR) is one of the main subjects of study in the areas of computer vision and machine learning due to the great benefits that can be achieved. Examples of the study areas are: health prevention, security and surveillance, automotive research, and many others. The proposed approaches are carried out using machine learning techniques and present good results. However, it is difficult to observe how the descriptors of human activities are grouped. In order to obtain a better understanding of the the behavior of descriptors, it is important to improve the abilities to recognize the human activities. This paper proposes a novel approach for the HAR based on acoustic data and similarity networks. In this approach, we were able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern. We evaluated the similarity of the sounds considering mainly two features: the sound location and the materials that were used. As a result, the materials are a good reference classifying the human activities compared with the location.

A Similarity Analysis of Audio Signal to Develop a Human Activity Recognition Using Similarity Networks

PubMed Central

García-Hernández, Alejandra; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Velasco-Elizondo, Perla; Cárdenas-Vargas, Rogelio

2017-01-01

Human Activity Recognition (HAR) is one of the main subjects of study in the areas of computer vision and machine learning due to the great benefits that can be achieved. Examples of the study areas are: health prevention, security and surveillance, automotive research, and many others. The proposed approaches are carried out using machine learning techniques and present good results. However, it is difficult to observe how the descriptors of human activities are grouped. In order to obtain a better understanding of the the behavior of descriptors, it is important to improve the abilities to recognize the human activities. This paper proposes a novel approach for the HAR based on acoustic data and similarity networks. In this approach, we were able to characterize the sound of the activities and identify those activities looking for similarity in the sound pattern. We evaluated the similarity of the sounds considering mainly two features: the sound location and the materials that were used. As a result, the materials are a good reference classifying the human activities compared with the location. PMID:29160799

Human errors and violations in computer and information security: the viewpoint of network administrators and security specialists.

PubMed

Kraemer, Sara; Carayon, Pascale

2007-03-01

This paper describes human errors and violations of end users and network administration in computer and information security. This information is summarized in a conceptual framework for examining the human and organizational factors contributing to computer and information security. This framework includes human error taxonomies to describe the work conditions that contribute adversely to computer and information security, i.e. to security vulnerabilities and breaches. The issue of human error and violation in computer and information security was explored through a series of 16 interviews with network administrators and security specialists. The interviews were audio taped, transcribed, and analyzed by coding specific themes in a node structure. The result is an expanded framework that classifies types of human error and identifies specific human and organizational factors that contribute to computer and information security. Network administrators tended to view errors created by end users as more intentional than unintentional, while errors created by network administrators as more unintentional than intentional. Organizational factors, such as communication, security culture, policy, and organizational structure, were the most frequently cited factors associated with computer and information security.

Tracing the temporal-spatial transcriptome landscapes of the human fetal digestive tract using single-cell RNA-sequencing.

PubMed

Gao, Shuai; Yan, Liying; Wang, Rui; Li, Jingyun; Yong, Jun; Zhou, Xin; Wei, Yuan; Wu, Xinglong; Wang, Xiaoye; Fan, Xiaoying; Yan, Jie; Zhi, Xu; Gao, Yun; Guo, Hongshan; Jin, Xiao; Wang, Wendong; Mao, Yunuo; Wang, Fengchao; Wen, Lu; Fu, Wei; Ge, Hao; Qiao, Jie; Tang, Fuchou

2018-06-01

The development of the digestive tract is critical for proper food digestion and nutrient absorption. Here, we analyse the main organs of the digestive tract, including the oesophagus, stomach, small intestine and large intestine, from human embryos between 6 and 25 weeks of gestation as well as the large intestine from adults using single-cell RNA-seq analyses. In total, 5,227 individual cells are analysed and 40 cell types clearly identified. Their crucial biological features, including developmental processes, signalling pathways, cell cycle, nutrient digestion and absorption metabolism, and transcription factor networks, are systematically revealed. Moreover, the differentiation and maturation processes of the large intestine are thoroughly investigated by comparing the corresponding transcriptome profiles between embryonic and adult stages. Our work offers a rich resource for investigating the gene regulation networks of the human fetal digestive tract and adult large intestine at single-cell resolution.

Singular structure of Mueller matrices images of biological crystal networks for diagnostic human tissues pathological changes

NASA Astrophysics Data System (ADS)

Sakhnovskiy, M. Y.; Ushenko, V. A.

2013-09-01

The process of converting of laser radiation by optically anisotropic crystals of biological networks are singular in the sense of total (simultaneous) of mechanisms of orientation and phase (birefringence) anisotropy the formation of polarization-inhomogeneous field of scattered radiation. This work is aimed at developing a method of polarization selection mechanisms of blood plasma polycrystalline networks anisotropy. The relationship between statistics, correlation and fractal parameters of polarization-inhomogeneous images of blood plasma and by linear dichroism and linear birefringence of polycrystalline networks albumin and globulin was found. The criteria of differentiation and diagnostic images of polarization-inhomogeneous plasma samples of the control group (donor) and a group of patients with malignant changes of breast tissue was identified.