scholarly journals The integration of large-scale public data and network analysis uncovers molecular characteristics of psoriasis

2021 ◽  
Author(s):  
Antonio Federico ◽  
Alisa Pavel ◽  
Lena Moebus ◽  
David McKean ◽  
Giusy del Giudice ◽  
...  
Keyword(s):  
Network Analysis ◽  
Network Models ◽  
Knowledge Generation ◽  
Integrated Analysis ◽  
Functional Information ◽  
Public Data ◽  
Wide Range ◽  
Uninvolved Skin ◽  
Unaffected Skin ◽  
Skin Samples

In recent years, a growing interest in the characterization of the molecular basis of psoriasis has been observed. However, despite the availability of a large amount of molecular data, many pathogenic mechanisms of psoriasis are still poorly understood. In this study, we performed an integrated analysis of 23 public transcriptomic datasets encompassing both lesional and uninvolved skin samples from psoriasis patients. We defined comprehensive gene co-expression network models of psoriatic lesions and uninvolved skin. Moreover, we collected, curated and exploited a wide range of functional information from multiple public sources in order to systematically annotate the inferred networks. The integrated transcriptomics analysis of public datasets shed light on a number of genes which are frequently deregulated in the psoriatic lesion compared with the unaffected skin in a large number of studies. In particular, CRABP2, LCN2, S100A12 and PDZK1IP1 were found to be deregulated in all of the datasets analyzed. Furthermore, the analysis of co-expression networks highlights genes showing aberrant patterns of connectivity in the lesional network as compared to the network inferred from unaffected skin samples. For instance, we identified co-expression patterns of SERPINB4, KYNU and S100A12 as being the most affected by the disease. Network analysis allowed us to identify YPEL1 and HUS1 as plausible, previously unknown, actors in the expression of the psoriasis phenotype. In addition, by exploiting topological properties of the network models, we highlighted a set of 250 non-deregulated genes, 223 of which have never been associated with the disease before, including CACNA1A, HADH, ATP5MC1 and CBARP among others. Finally, we characterized specific communities of co-expressed genes sustaining relevant molecular functions and specific immune cell types expression signatures playing a role in the psoriasis lesion. Overall, integrating experimental driven results with curated functional information from public repositories represents an efficient approach to empower knowledge generation about psoriasis and may be applicable to other complex diseases.

scholarly journals A Tutorial on Epistemic Network Analysis: Analyzing the Structure of Connections in Cognitive, Social, and Interaction Data

2016 ◽  
Vol 3 (3) ◽  
pp. 9-45 ◽  
Author(s):  
David Williamson Shaffer ◽  
Wesley Collier ◽  
A. R. Ruis
Keyword(s):  
Network Analysis ◽  
Quantitative Research ◽  
Dynamic Network ◽  
Network Models ◽  
Cognitive Networks ◽  
Habits Of Mind ◽  
Dynamic Network Models ◽  
Wide Range ◽  
Fixed Set ◽  
Dynamic Relationships

This paper provides a tutorial on epistemic network analysis (ENA), a novel method for identifying and quantifying connections among elements in coded data and representing them in dynamic network models. Such models illustrate the structure of connections and measure the strength of association among elements in a network, and they quantify changes in the composition and strength of connections over time. Importantly, ENA enables comparison of networks both directly and via summary statistics, so the method can be used to explore a wide range of qualitative and quantitative research questions in situations where patterns of association in data are hypothesized to be meaningful. While ENA was originally developed to model cognitive networks—the patterns of association between knowledge, skills, values, habits of mind, and other elements that characterize complex thinking—ENA is a robust method that can be used to model patterns of association in any system characterized by a complex network of dynamic relationships among a relatively small, fixed set of elements.

Career Development Theory: An Integrated Analysis

2020 ◽  
Author(s):  
Julia Yates
Keyword(s):  
Career Choice ◽  
Subject Matter ◽  
Academic Disciplines ◽  
Development Theory ◽  
Integrated Analysis ◽  
Theoretical Frameworks ◽  
Wide Range ◽  
Comprehensive Picture ◽  
The Subject ◽  
Career Practice

Career theories are developed to help make sense of the complexity of career choice and development. The intricacy of the subject matter is such that career theories most often focus on one or two aspects of the phenomenon. As such, the challenges of integrating the theories with each other, and integrating them within career practice, are not insignificant. In this chapter, an overview of the theoretical landscape is offered that illustrates how the theories align with each other to build up a comprehensive picture of career choice and development. The chapter introduces a wide range of theoretical frameworks, spanning seven decades and numerous academic disciplines, and discusses the most well-known theorists alongside less familiar names. The chapter is structured around four concepts: identity, environment, career learning, and psychological career resources. Suggestions are offered for the incorporation of theories in career practice.

scholarly journals Ligand-induced structural changes analysis of ribose-binding protein as studied by molecular dynamics simulations

2021 ◽  
pp. 1-12
Author(s):  
Haiyan Li ◽  
Zanxia Cao ◽  
Guodong Hu ◽  
Liling Zhao ◽  
Chunling Wang ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Structural Changes ◽  
Binding Protein ◽  
Network Models ◽  
Md Simulations ◽  
Protein Domain ◽  
Opening Angle ◽  
Conformation Changes ◽  
Wide Range ◽  
Ribose Binding Protein

BACKGROUND: The ribose-binding protein (RBP) from Escherichia coli is one of the representative structures of periplasmic binding proteins. Binding of ribose at the cleft between two domains causes a conformational change corresponding to a closure of two domains around the ligand. The RBP has been crystallized in the open and closed conformations. OBJECTIVE: With the complex trajectory as a control, our goal was to study the conformation changes induced by the detachment of the ligand, and the results have been revealed from two computational tools, MD simulations and elastic network models. METHODS: Molecular dynamics (MD) simulations were performed to study the conformation changes of RBP starting from the open-apo, closed-holo and closed-apo conformations. RESULTS: The evolution of the domain opening angle θ clearly indicates large structural changes. The simulations indicate that the closed states in the absence of ribose are inclined to transition to the open states and that ribose-free RBP exists in a wide range of conformations. The first three dominant principal motions derived from the closed-apo trajectories, consisting of rotating, bending and twisting motions, account for the major rearrangement of the domains from the closed to the open conformation. CONCLUSIONS: The motions showed a strong one-to-one correspondence with the slowest modes from our previous study of RBP with the anisotropic network model (ANM). The results obtained for RBP contribute to the generalization of robustness for protein domain motion studies using either the ANM or PCA for trajectories obtained from MD.

scholarly journals A Brief Overview of Social Network Analysis and its Current State within Romanian Sociology

2013 ◽  
Vol 3 (3) ◽  
pp. 5-11
Author(s):  
Marian-Gabriel Hâncean
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
International Relations ◽  
Early Spring ◽  
Research Topics ◽  
Political Sciences ◽  
Current State ◽  
Wide Range ◽  
Network Studies

Abstract The field of social network studies has been growing within the last 40 years, gathering scholars from a wide range of disciplines (biology, chemistry, geography, international relations, mathematics, political sciences, sociology etc.) and covering diverse substantive research topics. Using Google metrics, the scientific production within the field it is shown to follow an ascending trend since the late 60s. Within the Romanian sociology, social network analysis is still in his early spring, network studies being low in number and rather peripheral. This note gives a brief overview of social network analysis and makes some short references to the current state of the network studies within Romanian sociology

Acoustic Energy Balance During the Onset, Growth and Saturation of Thermoacoustic Instabilities

2020 ◽  
Author(s):  
R. Gaudron ◽  
D. Yang ◽  
A. S. Morgans
Keyword(s):  
Energy Balance ◽  
Network Models ◽  
Acoustic Energy ◽  
Dimensionless Numbers ◽  
Weakly Nonlinear ◽  
Acoustic Damping ◽  
Thermoacoustic Instabilities ◽  
Wide Range ◽  
Acoustic Absorption Coefficient ◽  
Flame Describing Function

Abstract Thermoacoustic instabilities can occur in a wide range of combustors and are prejudicial since they can lead to increased mechanical fatigue or even catastrophic failure. A well-established formalism to predict the onset, growth and saturation of such instabilities is based on acoustic network models. This approach has been successfully employed to predict the frequency and amplitude of limit cycle oscillations in a variety of combustors. However, it does not provide any physical insight in terms of the acoustic energy balance of the system. On the other hand, Rayleigh’s criterion may be used to quantify the losses, sources and transfers of acoustic energy within and at the boundaries of a combustor. However, this approach is cumbersome for most applications because it requires computing volume and surface integrals and averaging over an oscillation cycle. In this work, a new methodology for studying the acoustic energy balance of a combustor during the onset, growth and saturation of thermoacoustic instabilities is proposed. The two cornerstones of this new framework are the acoustic absorption coefficient Δ and the cycle-to-cycle acoustic energy ratio λ, both of which do not require computing integrals. Used along with a suitable acoustic network model, where the flame frequency response is described using the weakly nonlinear Flame Describing Function (FDF) formalism, these two dimensionless numbers are shown to characterize: 1) the variation of acoustic energy stored within the combustor between two consecutive cycles, 2) the acoustic energy transfers occurring at the combustor’s boundaries and 3) the sources and sinks of acoustic energy located within the combustor. The acoustic energy balance of the well-documented Palies burner is then analyzed during the onset, growth and saturation of thermoacoustic instabilities using this new methodology. It is demonstrated that this new approach allows a deeper understanding of the physical mechanisms at play. For instance, it is possible to determine when the flame acts as an acoustic energy source or sink, where acoustic damping is generated, and if acoustic energy is transmitted through the boundaries of the burner.

Abstract 313: Visualizing Out-Of-Hospital Cardiac Arrest in the Young: A Network Analysis of the Cardiac Arrest Registry for Enhanced Survival in Chicago

Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Alyssa Vermeulen ◽  
Marina Del Rios ◽  
Teri L Campbell ◽  
Hai Nguyen ◽  
Hoang H Nguyen
Keyword(s):  
Cardiac Arrest ◽  
Network Analysis ◽  
Emergency Room ◽  
American Indians ◽  
Network Models ◽  
Spontaneous Circulation ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Complex Network Models ◽  
Hospital Cardiac Arrest

Introduction: The interactions of various variables on out-of-hospital cardiac arrest (OHCA) in the young (1-35 years old) outcomes are complex. Network models have emerged as a way to abstract complex systems and gain insights into relational patterns among observed variables. Hypothesis: Network analysis helps provide qualitative and quantitative insights into how various variables interact with each other and affect outcomes in OHCA in the young. Methods: A mixed graphical network analysis was performed using variables collected by CARES. The network allows the visualization and quantification of each unique interaction between two variables that cannot be explained away by other variables in the data set. The strength of the underlying interaction is proportional to the thickness of the connections (edges) between the variables (nodes). We used the mgm package in R. Results: Figure 1 shows the network of the OHCA in the young cases in Chicago from 2013 to 2017. There are apparent clusters. Sustained return of spontaneous circulation and hypothermia are strongly correlated with survival and neurological outcomes. This cluster is in turn connected to the rest of the network by survival to emergency room. The interaction between any two variables can also be quantified. For example, American Indians cases occur more often in disadvantaged locations when compared to Whites (OR 4.5). The network also predicts how much one node can be explained by adjacent nodes. Only 20% of survival to emergency room is explained by its adjacent nodes. The remaining 80% is attributed to variables not represented in this network. This suggests that interventions to improve this node is difficult unless further data is available. Conclusion: Network analysis provides both a qualitative and quantitative evaluation of the complex system governing OHCA in the young. The networks predictive capability could help in identifying the most effective interventions to improve outcomes.

scholarly journals Impact of clock-associated Arabidopsis pseudo-response regulators in metabolic coordination

2009 ◽  
Vol 106 (17) ◽  
pp. 7251-7256 ◽  
Author(s):  
Atsushi Fukushima ◽  
Miyako Kusano ◽  
Norihito Nakamichi ◽  
Makoto Kobayashi ◽  
Naomi Hayashi ◽  
...  
Keyword(s):  
Circadian Clock ◽  
Stress Responses ◽  
Clock Genes ◽  
Higher Plants ◽  
Tricarboxylic Acid Cycle ◽  
Integrated Analysis ◽  
Response Regulators ◽  
Triple Mutant ◽  
Mitochondrial Functions ◽  
Wide Range

In higher plants, the circadian clock controls a wide range of cellular processes such as photosynthesis and stress responses. Understanding metabolic changes in arrhythmic plants and determining output-related function of clock genes would help in elucidating circadian-clock mechanisms underlying plant growth and development. In this work, we investigated physiological relevance of PSEUDO-RESPONSE REGULATORS (PRR 9, 7, and 5) in Arabidopsis thaliana by transcriptomic and metabolomic analyses. Metabolite profiling using gas chromatography–time-of-flight mass spectrometry demonstrated well-differentiated metabolite phenotypes of seven mutants, including two arrhythmic plants with similar morphology, a PRR 9, 7, and 5 triple mutant and a CIRCADIAN CLOCK-ASSOCIATED 1 (CCA1)-overexpressor line. Despite different light and time conditions, the triple mutant exhibited a dramatic increase in intermediates in the tricarboxylic acid cycle. This suggests that proteins PRR 9, 7, and 5 are involved in maintaining mitochondrial homeostasis. Integrated analysis of transcriptomics and metabolomics revealed that PRR 9, 7, and 5 negatively regulate the biosynthetic pathways of chlorophyll, carotenoid and abscisic acid, and α-tocopherol, highlighting them as additional outputs of pseudo-response regulators. These findings indicated that mitochondrial functions are coupled with the circadian system in plants.

scholarly journals ARCHITECTURE OF A NEURAL NETWORK ANALYZER OF BIOMETRIC PARAMETERS OF LISTENERS OF THE DISTANCE LEARNING SYSTEM

2020 ◽  
pp. 91-99
Author(s):  
Liudmyla Tereikovska
Keyword(s):  
Neural Network ◽  
Distance Learning ◽  
Network Analysis ◽  
Learning System ◽  
Integrated Analysis ◽  
Network Analyzer ◽  
Neural Network Analysis ◽  
Biometric Parameters ◽  
The Neural Network ◽  
Distance Learning System

The urgency of the task of developing tools for neural network analysis of biometric parameters for recognizing the personality and emotions of students of the distance learning system has been substantiated. The necessity of formalizing the architectural solutions used in the creation of software for neural network analysis of biometric parameters is shown. As a result of the research carried out in terms of the UML modeling language, the architecture of the neural network analyzer of biometric parameters has been developed. Diagrams of options for using the neural network analyzer have been developed both for recognizing the personality of a student when entering the system, and for recognizing the personality and emotions of a student in the process of his interaction with the distance learning system. Also, based on the developed use case diagrams, a structural diagram of the analyzer is built. The necessity of including subsystems for determining the functional parameters of the analyzer, registration of biometric parameters, neural network analysis of registered biometric parameters, personality recognition and emotion recognition is substantiated. An original feature of the proposed architectural solutions is the introduction into the neural network analysis subsystem of an integrated analysis module designed to summarize the results of neural network analysis separately for each of the biometric parameters. A rule for making an integrated decision has been developed, taking into account the results of a neural network analysis of each of the registered biometric parameters and the corresponding weight coefficients determined by expert evaluation. The introduction of the integrated analysis module makes it possible to increase the accuracy of recognition of emotions and personality of a student, since the final classification is realized through a generalized assessment of several guaranteed significant biometric parameters. In addition, the use of this module makes it possible to increase the reliability of the neural network analyzer in case of difficulties associated with the registration of a particular biometric parameter. It has been established that the decision-making rule can be improved by using one or more neural networks in the integrated analysis module, designed to generalize the results of the neural network analysis of all registered biometric parameters. It is proposed to correlate the directions of further research with the development of appropriate neural network solutions.

scholarly journals Neural network models of the tactile system develop first-order units with spatially complex receptive fields

2017 ◽  
Author(s):  
Charlie W. Zhao ◽  
Mark J. Daley ◽  
J. Andrew Pruszynski
Keyword(s):  
Neural Network ◽  
Network Performance ◽  
Receptive Fields ◽  
Network Models ◽  
Network Architectures ◽  
Learning Tools ◽  
Neural Network Models ◽  
First Order ◽  
Wide Range ◽  
Tactile System

AbstractFirst-order tactile neurons have spatially complex receptive fields. Here we use machine learning tools to show that such complexity arises for a wide range of training sets and network architectures, and benefits network performance, especially on more difficult tasks and in the presence of noise. Our work suggests that spatially complex receptive fields are normatively good given the biological constraints of the tactile periphery.

scholarly journals Dynamical differential covariance recovers directional network structure in multiscale neural systems

2021 ◽  
Author(s):  
Yusi Chen ◽  
Burke Q Rosen ◽  
Terrence J Sejnowski
Keyword(s):  
Structural Connectivity ◽  
Network Models ◽  
Ground Truth ◽  
Neural Systems ◽  
Noise Tolerance ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Dynamical Models ◽  
Wide Range ◽  
Neural Interactions

Investigating causal neural interactions are essential to understanding sub- sequent behaviors. Many statistical methods have been used for analyzing neural activity, but efficiently and correctly estimating the direction of net- work interactions remains difficult. Here, we derive dynamical differential covariance (DDC), a new method based on dynamical network models that detects directional interactions with low bias and high noise tolerance with- out the stationary assumption. The method is first validated on networks with false positive motifs and multiscale neural simulations where the ground truth connectivity is known. Then, applying DDC to recordings of resting-state functional magnetic resonance imaging (rs-fMRI) from over 1,000 individual subjects, DDC consistently detected regional interactions with strong structural connectivity. DDC can be generalized to a wide range of dynamical models and recording techniques.

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