scholarly journals Analysis and Classification of Word Co-Occurrence Networks From Alzheimer’s Patients and Controls

2021 ◽  
Vol 3 ◽  
Author(s):  
Tristan Millington ◽  
Saturnino Luz
Keyword(s):  
Word Frequency ◽  
Mean Squared Error ◽  
Cognitive Test ◽  
Edge Density ◽  
Network Embedding ◽  
Embedding Method ◽  
Control Networks ◽  
Network Measures ◽  
Network Properties ◽  
And Control

In this paper we construct word co-occurrence networks from transcript data of controls and patients with potential Alzheimer’s disease using the ADReSS challenge dataset of spontaneous speech. We examine measures of the structure of these networks for significant differences, finding that networks from Alzheimer’s patients have a lower heterogeneity and centralization, but a higher edge density. We then use these measures, a network embedding method and some measures from the word frequency distribution to classify the transcripts into control or Alzheimer’s, and to estimate the cognitive test score of a participant based on the transcript. We find it is possible to distinguish between the AD and control networks on structure alone, achieving 66.7% accuracy on the test set, and to predict cognitive scores with a root mean squared error of 5.675. Using the network measures is more successful than using the network embedding method. However, if the networks are shuffled we find relatively few of the measures are different, indicating that word frequency drives many of the network properties. This observation is borne out by the classification experiments, where word frequency measures perform similarly to the network measures.

scholarly journals Predicting Empathy from Resting Brain Connectivity

2019 ◽  
Author(s):  
Leonardo Christov-Moore ◽  
Nicco Reggente ◽  
Pamela K. Douglas ◽  
Jamie D. Feusner ◽  
Marco Iacoboni
Keyword(s):  
Sex Differences ◽  
Visual System ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Task Demands ◽  
Empathic Concern ◽  
Control Networks ◽  
Biological Sex ◽  
Network Properties ◽  
And Control

AbstractRecent studies suggest that individual differences in empathic concern may be mediated by continuous interactions between self-other resonance and cognitive control networks. To test this hypothesis, we used machine learning to examine whether resting fMRI connectivity (i.e. the degree of synchronous BOLD activity across multiple cortical areas in the absence of task demands) of resonance and control networks could predict trait empathy (n=58). Indeed, resonance and control networks’ interconnectivity predicted empathic concern. Empathic concern was also predicted by connectivity within the somatomotor network. In light of numerous reported sex differences in empathy, we controlled for biological sex and also studied separately what aspect of these features could predict participants’ sex. Sex was best predicted by the interconnectivity of the visual system with the resonance, somatomotor, and cingulo-opercular network, as well as the somatomotor-control network connectivity. These findings confirm that variation in empathic responses to others reflects characteristic network properties detectable regardless of task demands. Furthermore, network properties of the visual system may be a locus of sex differences previously unaccounted for in empathy research. Finally, these findings suggest that it may be possible to assess empathic predispositions in individuals without needing to perform conventional empathy assessments.

Network Embedding on Hierarchical Community Structure Network

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Guojie Song ◽  
Yun Wang ◽  
Lun Du ◽  
Yi Li ◽  
Junshan Wang
Keyword(s):  
Community Structure ◽  
Structural Information ◽  
Spherical Surface ◽  
Network Embedding ◽  
The Galaxy ◽  
Community Information ◽  
The Hierarchical Structure ◽  
Network Properties ◽  
Multi Class Classification ◽  
Low Dimensional

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable the network to be easily analyzed at various scales. Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding. More specifically, we present an approach of embedding communities into a low-dimensional spherical surface, the center of which represents the parent community they belong to. Our experiments reveal that the representations from GNE preserve the hierarchical community structure and show advantages in several applications such as vertex multi-class classification, network visualization, and link prediction. The source code of GNE is available online.

scholarly journals A Conceptual Model of Measurement Uncertainty in IoT Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1827
Author(s):  
Piotr Cofta ◽  
Kostas Karatzas ◽  
Cezary Orłowski
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Reference Model ◽  
Primary Objective ◽  
Theory And Practice ◽  
Fitness For Purpose ◽  
Industrial Practice ◽  
Control Networks ◽  
And Control ◽  
Measurement And Control

The growing popularity of inexpensive IoT (Internet of Things) sensor networks makes their uncertainty an important aspect of their adoption. The uncertainty determines their fitness for purpose, their perceived quality and the usefulness of information they provide. Nevertheless, neither the theory nor the industrial practice of uncertainty offer a coherent answer on how to address uncertainty of networks of this type and their components. The primary objective of this paper is to facilitate the discussion of what progress should be made regarding the theory and the practice of uncertainty of IoT sensor networks to satisfy current needs. This paper provides a structured overview of uncertainty, specifically focusing on IoT sensor networks. It positions IoT sensor networks as contrasted with professional measurement and control networks and presents their conceptual sociotechnical reference model. The reference model advises on the taxonomy of uncertainty proposed in this paper that demonstrates semantic differences between various views on uncertainty. This model also allows for identifying key challenges that should be addressed to improve the theory and practice of uncertainty in IoT sensor networks.

scholarly journals Distinct network topology in Alzheimer’s disease and behavioral variant frontotemporal dementia

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Adeline Su Lyn Ng ◽  
Juan Wang ◽  
Kwun Kei Ng ◽  
Joanna Su Xian Chong ◽  
Xing Qian ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Network Topology ◽  
Neuropsychiatric Symptoms ◽  
Brain Network ◽  
Salience Network ◽  
Behavioral Variant Frontotemporal Dementia ◽  
Control Networks ◽  
And Control

Abstract Background Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) cause distinct atrophy and functional disruptions within two major intrinsic brain networks, namely the default network and the salience network, respectively. It remains unclear if inter-network relationships and whole-brain network topology are also altered and underpin cognitive and social–emotional functional deficits. Methods In total, 111 participants (50 AD, 14 bvFTD, and 47 age- and gender-matched healthy controls) underwent resting-state functional magnetic resonance imaging (fMRI) and neuropsychological assessments. Functional connectivity was derived among 144 brain regions of interest. Graph theoretical analysis was applied to characterize network integration, segregation, and module distinctiveness (degree centrality, nodal efficiency, within-module degree, and participation coefficient) in AD, bvFTD, and healthy participants. Group differences in graph theoretical measures and empirically derived network community structures, as well as the associations between these indices and cognitive performance and neuropsychiatric symptoms, were subject to general linear models, with age, gender, education, motion, and scanner type controlled. Results Our results suggested that AD had lower integration in the default and control networks, while bvFTD exhibited disrupted integration in the salience network. Interestingly, AD and bvFTD had the highest and lowest degree of integration in the thalamus, respectively. Such divergence in topological aberration was recapitulated in network segregation and module distinctiveness loss, with AD showing poorer modular structure between the default and control networks, and bvFTD having more fragmented modules in the salience network and subcortical regions. Importantly, aberrations in network topology were related to worse attention deficits and greater severity in neuropsychiatric symptoms across syndromes. Conclusions Our findings underscore the reciprocal relationships between the default, control, and salience networks that may account for the cognitive decline and neuropsychiatric symptoms in dementia.

scholarly journals Real-Time Detection of Dictionary DGA Network Traffic Using Deep Learning

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Kate Highnam ◽  
Domenic Puzio ◽  
Song Luo ◽  
Nicholas R. Jennings
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Traffic ◽  
Short Term Memory ◽  
Domain Names ◽  
Control Networks ◽  
Detection Techniques ◽  
Lstm Network ◽  
And Control

AbstractBotnets and malware continue to avoid detection by static rule engines when using domain generation algorithms (DGAs) for callouts to unique, dynamically generated web addresses. Common DGA detection techniques fail to reliably detect DGA variants that combine random dictionary words to create domain names that closely mirror legitimate domains. To combat this, we created a novel hybrid neural network, Bilbo the “bagging” model, that analyses domains and scores the likelihood they are generated by such algorithms and therefore are potentially malicious. Bilbo is the first parallel usage of a convolutional neural network (CNN) and a long short-term memory (LSTM) network for DGA detection. Our unique architecture is found to be the most consistent in performance in terms of AUC, $$F_1$$ F 1 score, and accuracy when generalising across different dictionary DGA classification tasks compared to current state-of-the-art deep learning architectures. We validate using reverse-engineered dictionary DGA domains and detail our real-time implementation strategy for scoring real-world network logs within a large enterprise. In 4 h of actual network traffic, the model discovered at least five potential command-and-control networks that commercial vendor tools did not flag.

scholarly journals MicroRNAs in Autoimmunity and Hematological Malignancies

2018 ◽  
Vol 19 (10) ◽  
pp. 3139 ◽  
Author(s):  
Mirco Marco ◽  
Alice Ramassone ◽  
Sara Pagotto ◽  
Eleni Anastasiadou ◽  
Angelo Veronese ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Hematological Malignancies ◽  
Immune Cell ◽  
Immunological Tolerance ◽  
Considerable Effort ◽  
Control Networks ◽  
Immune Cell Subsets ◽  
Bidirectional Relationship ◽  
Relevant Role ◽  
And Control

Autoimmunity and hematological malignancies are often concomitant in patients. A causal bidirectional relationship exists between them. Loss of immunological tolerance with inappropriate activation of the immune system, likely due to environmental and genetic factors, can represent a breeding ground for the appearance of cancer cells and, on the other hand, blood cancers are characterized by imbalanced immune cell subsets that could support the development of the autoimmune clone. Considerable effort has been made for understanding the proteins that have a relevant role in both processes; however, literature advances demonstrate that microRNAs (miRNAs) surface as the epigenetic regulators of those proteins and control networks linked to both autoimmunity and hematological malignancies. Here we review the most up-to-date findings regarding the miRNA-based molecular mechanisms that underpin autoimmunity and hematological malignancies.

scholarly journals Interactions between default mode and control networks as a function of increasing cognitive reasoning complexity

2015 ◽  
Vol 36 (7) ◽  
pp. 2719-2731 ◽  
Author(s):  
Luke Hearne ◽  
Luca Cocchi ◽  
Andrew Zalesky ◽  
Jason B. Mattingley
Keyword(s):  
Default Mode ◽  
Control Networks ◽  
Cognitive Reasoning ◽  
And Control

Estimating Treatment Effects in the Presence of Noncompliance and Nonresponse: The Generalized Endogenous Treatment Model

2011 ◽  
Vol 19 (2) ◽  
pp. 205-226 ◽  
Author(s):  
Kevin M. Esterling ◽  
Michael A. Neblo ◽  
David M. J. Lazer
Keyword(s):  
Treatment Effect ◽  
Instrumental Variable ◽  
Treatment Effects ◽  
Mean Squared Error ◽  
Principal Stratification ◽  
Squared Error ◽  
Treatment Effect Heterogeneity ◽  
Causal Treatment ◽  
Effect Heterogeneity ◽  
And Control

If ignored, noncompliance with a treatment or nonresponse on outcome measures can bias estimates of treatment effects in a randomized experiment. To identify and estimate causal treatment effects in the case where compliance and response depend on unobservables, we propose the parametric generalized endogenous treatment (GET) model. GET incorporates behavioral responses within an experiment to measure each subject's latent compliance type and identifies causal effects via principal stratification. Using simulation methods and an application to field experimental data, we show GET has a dramatically lower mean squared error for treatment effect estimates than existing approaches to principal stratification that impute, rather than measure, compliance type. In addition, we show that GET allows one to relax and test the instrumental variable exclusion restriction assumption, to test for the presence of treatment effect heterogeneity across a range of compliance types, and to test for treatment ignorability when treatment and control samples are balanced on observable covariates.

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