Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.

(DOE/Pacific Northwest National Laboratory) Software vulnerabilities are more likely to be discussed on social media before they're revealed on a government reporting site, a practice that could pose a national security threat, according to computer scientists at Pacific Northwest National Laboratory.

(University of Tsukuba) Researchers from the University of Tsukuba discovered that fish schools showed a significant change in behavior with varying school sizes. Using integrated information theory, they showed that a significant change in the interaction between the fish and the overall collective behavior occurred between three- and four-fish schools, including the emergence of leadership within the group. These findings help understand the dynamics of collective behavior.

Eelke P. Béguin
Apr 24, 2020; 0:RA120.001964v1-mcp.RA120.001964
Research

Xiao-Kang Lun
May 1, 2020; 19:744-756
Review

Background: Functional magnetic resonance imaging (fMRI) studies have reported altered integrity of large-scale neurocognitive networks (NCNs) in dementing disorders. However, findings on specificity of these alterations in patients with Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are still very limited. Recently, NCNs have been successfully captured using positron emission tomography (PET) with F18-fluordesoxyglucose (FDG). Methods: Network integrity was measured in 72 individuals (38 male) with mild AD, bvFTD, and healthy controls using a simultaneous resting state fMRI and FDG-PET. Indices of network integrity were calculated for each subject, network, and imaging modality. Results: In either modality, independent component analysis revealed four major NCNs: anterior default mode network (DMN), posterior DMN, salience network, and right central executive network (CEN). In fMRI data, integrity of posterior DMN was found to be significantly reduced in both patient groups relative to controls. In the AD group anterior DMN and CEN appeared to be additionally affected. In PET data, only integrity of posterior DMN in patients with AD was reduced, while three remaining networks appeared to be affected only in patients with bvFTD. In a logistic regression analysis, integrity of anterior DMN as measured with PET alone accurately differentiated between the patient groups. A correlation between indices of two imaging modalities was overall low. Conclusion: FMRI and FDG-PET capture partly different aspects of network integrity. A higher disease specificity of NCNs as derived from PET data supports metabolic connectivity imaging as a promising diagnostic tool.

Signaling networks process intra- and extracellular information to modulate the functions of a cell. Deregulation of signaling networks results in abnormal cellular physiological states and often drives diseases. Network responses to a stimulus or a drug treatment can be highly heterogeneous across cells in a tissue because of many sources of cellular genetic and non-genetic variance. Signaling network heterogeneity is the key to many biological processes, such as cell differentiation and drug resistance. Only recently, the emergence of multiplexed single-cell measurement technologies has made it possible to evaluate this heterogeneity. In this review, we categorize currently established single-cell signaling network profiling approaches by their methodology, coverage, and application, and we discuss the advantages and limitations of each type of technology. We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network analyses.

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The vessel wall is continuously exposed to hemodynamic forces generated by blood flow. Endothelial mechanosensors perceive and translate mechanical signals via cellular signaling pathways into biological processes that control endothelial development, phenotype and function. To assess the hemodynamic effects on the endothelium on a system-wide level, we applied a quantitative mass spectrometry approach combined with cell surface chemical footprinting. SILAC-labeled endothelial cells were subjected to flow-induced shear stress for 0, 24 or 48 hours, followed by chemical labeling of surface proteins using a non-membrane permeable biotin label, and analysis of the whole proteome and the cell surface proteome by LC-MS/MS analysis. These studies revealed that of the >5000 quantified proteins 104 were altered, which were highly enriched for extracellular matrix proteins and proteins involved in cell-matrix adhesion. Cell surface proteomics indicated that LAMA4 was proteolytically processed upon flow-exposure, which corresponded to the decreased LAMA4 mass observed on immunoblot. Immunofluorescence microscopy studies highlighted that the endothelial basement membrane was drastically remodeled upon flow exposure. We observed a network-like pattern of LAMA4 and LAMA5, which corresponded to the localization of laminin-adhesion molecules ITGA6 and ITGB4. Furthermore, the adaptation to flow-exposure did not affect the inflammatory response to tumor necrosis factor α, indicating that inflammation and flow trigger fundamentally distinct endothelial signaling pathways with limited reciprocity and synergy. Taken together, this study uncovers the blood flow-induced remodeling of the basement membrane and stresses the importance of the subendothelial basement membrane in vascular homeostasis.

Lena Eliasson
May 1, 2020; 69:804-812
Small Noncoding RNAs in Diabetes

Given diverging demographics, rising educational attainment and wide variation in economic opportunities, countries in the Association of Southeast Asian Nations are poised to see an expansion of both the demand for and supply of skilled migrants willing and able to move. The convergence of these megatrends represents unique opportunities for human-capital development and brain circulation, as this report explores.

Latent space models are effective tools for statistical modeling and visualization of network data. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing the likelihood function for an existing class of inner-product models, and we establish their statistical rates of convergence for these models. In addition, the theory informs us that both methods work simultaneously for a wide range of different latent space models that allow latent positions to affect edge formation in flexible ways, such as distance models. Furthermore, the effectiveness of the methods is demonstrated on a number of real world network data sets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.

Recurrent Neural Networks have been widely used to process sequence data, but have long been criticized for their biological implausibility and training difficulties related to vanishing and exploding gradients. This paper presents a novel algorithm for training recurrent networks, target propagation through time (TPTT), that outperforms standard backpropagation through time (BPTT) on four out of the five problems used for testing. The proposed algorithm is initially tested and compared to BPTT on four synthetic time lag tasks, and its performance is also measured using the sequential MNIST data set. In addition, as TPTT uses target propagation, it allows for discrete nonlinearities and could potentially mitigate the credit assignment problem in more complex recurrent architectures.

In this paper we introduce a statistical model, called additively faithful directed acyclic graph (AFDAG), for causal learning from observational data. Our approach is based on additive conditional independence (ACI), a recently proposed three-way statistical relation that shares many similarities with conditional independence but without resorting to multi-dimensional kernels. This distinct feature strikes a balance between a parametric model and a fully nonparametric model, which makes the proposed model attractive for handling large networks. We develop an estimator for AFDAG based on a linear operator that characterizes ACI, and establish the consistency and convergence rates of this estimator, as well as the uniform consistency of the estimated DAG. Moreover, we introduce a modified PC-algorithm to implement the estimating procedure efficiently, so that its complexity is determined by the level of sparseness rather than the dimension of the network. Through simulation studies we show that our method outperforms existing methods when commonly assumed conditions such as Gaussian or Gaussian copula distributions do not hold. Finally, the usefulness of AFDAG formulation is demonstrated through an application to a proteomics data set.

Sean L. Simpson, F. DuBois Bowman, Paul J. Laurienti

Source: Statist. Surv., Volume 7, 1--36.

Abstract:
Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.

Spatial trajectories are ubiquitous and complex signals. Their analysis is crucial in many research fields, from urban planning to neuroscience. Several approaches have been proposed to cluster trajectories. They rely on hand-crafted features, which struggle to capture the spatio-temporal complexity of the signal, or on Artificial Neural Networks (ANNs) which can be more efficient but less interpretable. In this paper we present a novel ANN architecture designed to capture the spatio-temporal patterns characteristic of a set of trajectories, while taking into account the demographics of the navigators. Hence, our model extracts markers linked to both behaviour and demographics. We propose a composite signal analyser (CompSNN) combining three simple ANN modules. Each of these modules uses different signal representations of the trajectory while remaining interpretable. Our CompSNN performs significantly better than its modules taken in isolation and allows to visualise which parts of the signal were most useful to discriminate the trajectories.

A deep neural network has relieved the burden of feature engineering by human experts, but comparable efforts are instead required to determine an effective architecture. On the other hands, as the size of a network has over-grown, a lot of resources are also invested to reduce its size. These problems can be addressed by sparsification of an over-complete model, which removes redundant parameters or connections by pruning them away after training or encouraging them to become zero during training. In general, however, these approaches are not fully differentiable and interrupt an end-to-end training process with the stochastic gradient descent in that they require either a parameter selection or a soft-thresholding step. In this paper, we propose a fully differentiable sparsification method for deep neural networks, which allows parameters to be exactly zero during training, and thus can learn the sparsified structure and the weights of networks simultaneously using the stochastic gradient descent. We apply the proposed method to various popular models in order to show its effectiveness.

In classification models fairness can be ensured by solving a constrained optimization problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and Equalized Odds, which are non-decomposable and non-convex. Researchers define convex surrogates of the constraints and then apply convex optimization frameworks to obtain fair classifiers. Surrogates serve only as an upper bound to the actual constraints, and convexifying fairness constraints might be challenging.

We propose a neural network-based framework, emph{FNNC}, to achieve fairness while maintaining high accuracy in classification. The above fairness constraints are included in the loss using Lagrangian multipliers. We prove bounds on generalization errors for the constrained losses which asymptotically go to zero. The network is optimized using two-step mini-batch stochastic gradient descent. Our experiments show that FNNC performs as good as the state of the art, if not better. The experimental evidence supplements our theoretical guarantees. In summary, we have an automated solution to achieve fairness in classification, which is easily extendable to many fairness constraints.

We develop a general framework for data-driven approximation of input-output maps between infinite-dimensional spaces. The proposed approach is motivated by the recent successes of neural networks and deep learning, in combination with ideas from model reduction. This combination results in a neural network approximation which, in principle, is defined on infinite-dimensional spaces and, in practice, is robust to the dimension of finite-dimensional approximations of these spaces required for computation. For a class of input-output maps, and suitably chosen probability measures on the inputs, we prove convergence of the proposed approximation methodology. Numerically we demonstrate the effectiveness of the method on a class of parametric elliptic PDE problems, showing convergence and robustness of the approximation scheme with respect to the size of the discretization, and compare our method with existing algorithms from the literature.

Eric D. Kolaczyk, Lizhen Lin, Steven Rosenberg, Jackson Walters, Jie Xu.

Source: The Annals of Statistics, Volume 48, Number 1, 514--538.

Abstract:
It is becoming increasingly common to see large collections of network data objects, that is, data sets in which a network is viewed as a fundamental unit of observation. As a result, there is a pressing need to develop network-based analogues of even many of the most basic tools already standard for scalar and vector data. In this paper, our focus is on averages of unlabeled, undirected networks with edge weights. Specifically, we (i) characterize a certain notion of the space of all such networks, (ii) describe key topological and geometric properties of this space relevant to doing probability and statistics thereupon, and (iii) use these properties to establish the asymptotic behavior of a generalized notion of an empirical mean under sampling from a distribution supported on this space. Our results rely on a combination of tools from geometry, probability theory and statistical shape analysis. In particular, the lack of vertex labeling necessitates working with a quotient space modding out permutations of labels. This results in a nontrivial geometry for the space of unlabeled networks, which in turn is found to have important implications on the types of probabilistic and statistical results that may be obtained and the techniques needed to obtain them.

Subhadeep Paul, Yuguo Chen.

Source: The Annals of Statistics, Volume 48, Number 1, 230--250.

Abstract:
We consider the problem of estimating a consensus community structure by combining information from multiple layers of a multi-layer network using methods based on the spectral clustering or a low-rank matrix factorization. As a general theme, these “intermediate fusion” methods involve obtaining a low column rank matrix by optimizing an objective function and then using the columns of the matrix for clustering. However, the theoretical properties of these methods remain largely unexplored. In the absence of statistical guarantees on the objective functions, it is difficult to determine if the algorithms optimizing the objectives will return good community structures. We investigate the consistency properties of the global optimizer of some of these objective functions under the multi-layer stochastic blockmodel. For this purpose, we derive several new asymptotic results showing consistency of the intermediate fusion techniques along with the spectral clustering of mean adjacency matrix under a high dimensional setup, where the number of nodes, the number of layers and the number of communities of the multi-layer graph grow. Our numerical study shows that the intermediate fusion techniques outperform late fusion methods, namely spectral clustering on aggregate spectral kernel and module allegiance matrix in sparse networks, while they outperform the spectral clustering of mean adjacency matrix in multi-layer networks that contain layers with both homophilic and heterophilic communities.

Joe Watson, James V. Zidek, Gavin Shaddick.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2662--2700.

Abstract:
This paper presents a general model framework for detecting the preferential sampling of environmental monitors recording an environmental process across space and/or time. This is achieved by considering the joint distribution of an environmental process with a site-selection process that considers where and when sites are placed to measure the process. The environmental process may be spatial, temporal or spatio-temporal in nature. By sharing random effects between the two processes, the joint model is able to establish whether site placement was stochastically dependent of the environmental process under study. Furthermore, if stochastic dependence is identified between the two processes, then inferences about the probability distribution of the spatio-temporal process will change, as will predictions made of the process across space and time. The embedding into a spatio-temporal framework also allows for the modelling of the dynamic site-selection process itself. Real-world factors affecting both the size and location of the network can be easily modelled and quantified. Depending upon the choice of the population of locations considered for selection across space and time under the site-selection process, different insights about the precise nature of preferential sampling can be obtained. The general framework developed in the paper is designed to be easily and quickly fit using the R-INLA package. We apply this framework to a case study involving particulate air pollution over the UK where a major reduction in the size of a monitoring network through time occurred. It is demonstrated that a significant response-biased reduction in the air quality monitoring network occurred, namely the relocation of monitoring sites to locations with the highest pollution levels, and the routine removal of sites at locations with the lowest. We also show that the network was consistently unrepresenting levels of particulate matter seen across much of GB throughout the operating life of the network. Finally we show that this may have led to a severe overreporting of the population-average exposure levels experienced across GB. This could have great impacts on estimates of the health effects of black smoke levels.

Silvia Metelli, Nicholas Heard.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2586--2610.

Abstract:
Monitoring computer network traffic for anomalous behaviour presents an important security challenge. Arrivals of new edges in a network graph represent connections between a client and server pair not previously observed, and in rare cases these might suggest the presence of intruders or malicious implants. We propose a Bayesian model and anomaly detection method for simultaneously characterising existing network structure and modelling likely new edge formation. The method is demonstrated on real computer network authentication data and successfully identifies some machines which are known to be compromised.

William W. Seeley
Feb 28, 2007; 27:2349-2356
BehavioralSystemsCognitive

William W. Seeley
Feb 28, 2007; 27:2349-2356
BehavioralSystemsCognitive

Bank of Italy Working Papers by Giorgio Albareto, Andrea Cardillo, Andrea Hamaui and Giuseppe Marinelli

Family involvement in the Autism Intervention Research Network on Physical Health, the Autism Treatment Network, and the Autism Learning Health Network, jointly the Autism Networks, has evolved and grown into a meaningful and robust collaboration between families, providers, and researchers. Family involvement at the center of the networks includes both local and national network-wide coproduction and contribution. Family involvement includes actively co-authoring research proposals for large grants, equal membership of network committees and workgroups, and formulating quality improvement pathways for local recruitment efforts and other network initiatives. Although families are involved in every aspect of network activity, families have been the driving force of specifically challenging the networks to concentrate research, education, and dissemination efforts around 3 pillar initiatives of addressing comorbidities of anxiety, attention-deficit/hyperactivity disorder, and irritability in autism during the networks’ upcoming funding cycle. The expansion of the networks’ Extension for Community Healthcare Outcomes program is an exciting network initiative that brings best practices in autism care to community providers. As equal hub members of each Extension for Community Healthcare Outcomes team, families ensure that participants are intimately cognizant of family perspectives and goals. Self-advocacy involvement in the networks is emerging, with plans for each site to have self-advocacy representation by the spring of 2020 and ultimately forming their own coproduction committee. The Autism Treatment Network, the Autism Intervention Research Network on Physical Health, and the Autism Learning Health Network continue to be trailblazing organizations in how families are involved in the growth of their networks, production of meaningful research, and dissemination of information to providers and families regarding emerging work in autism spectrum disorders.

Trisomy 13 and 18 are conditions with 1-year survival rates of less than 10% and have traditionally been treated with palliative care. There are increasing reports of ethical dilemmas caused by parental requests for clinical interventions.

Parents who belong to social networks report an enriching family experience and describe surviving children as happy. Many of these parents describe challenging encounters with health care providers. (Read the full article)

Previous studies have suggested that health care providers, family members, friends, and others play a role in shaping parents’ vaccination decisions. Other research has suggested that the media can influence whether parents decide to vaccinate their children.

Through the application of social network analysis, this study formally examines and quantifies how parents are influenced by the people and sources around them. Its findings suggest that social networks are important, particularly for parents who do not completely vaccinate. (Read the full article)

Lebanon’s Ministry of Telecommunications has been authorized by the Cabinet to temporarily operate the country’s two cellular networks until a new tender is launched, Minister of Telecommunications Talal Hawat said Tuesday.

Alireza Mansouri and Fattaneh Taghiyareh: Human interactions and opinion exchanges lead to social opinion dynamics, which is well described by opinion formation models. In these models, a random parameter is usually considered as the system noise, indicating the individual's inexplicable opinion changes. This noise could be an indicator of any other influential factors, such as public media, affects, and emotions. We study phase transitions, changes from one social phase to another, for various noise levels in a discrete opinion formation model based on the social impact theory with a scale-free random network as its interaction network topology. We also generate another similar model using the concept of social power based on the agents' node degrees in the interaction network as an estimation for their persuasiveness and supportiveness strengths and compare both models from phase transition viewpoint. We show by agent-based simulation and analytical considerations how opinion phases, including majority and non-majority, are formed in terms of the initial population of agents in opinion groups and noise levels. Two factors affect the system phase in equilibrium when the noise level increases: breaking up more segregated groups and dominance of stochastic behavior of the agents on their deterministic behavior. In the high enough noise levels, the system reaches a non-majority phase in equilibrium, regardless of the initial combination of opinion groups. In relatively low noise levels, the original model and the model whose agents' strengths are proportional to their centrality have different behaviors. The presence of a few high-connected influential leaders in the latter model consequences a different behavior in reaching equilibrium phase and different thresholds of noise levels for phase transitions.

Katarzyna Growiec, Jakub Growiec and Bogumił Kamiński: We study the impact of endogenous creation and destruction of social ties in an artificial society on aggregate outcomes such as generalized trust, willingness to cooperate, social utility and economic performance. To this end we put forward a computational multi-agent model where agents of overlapping generations interact in a dynamically evolving social network. In the model, four distinct dimensions of individuals’ social capital: degree, centrality, heterophilous and homophilous interactions, determine their generalized trust and willingness to cooperate, altogether helping them achieve certain levels of social utility (i.e., utility from social contacts) and economic performance. We find that the stationary state of the simulated social network exhibits realistic small-world topology. We also observe that societies whose social networks are relatively frequently reconfigured, display relatively higher generalized trust, willingness to cooperate, and economic performance – at the cost of lower social utility. Similar outcomes are found for societies where social tie dissolution is relatively weakly linked to family closeness.

Szymon Talaga and Andrzej Nowak: Real-world social networks often exhibit high levels of clustering, positive degree assortativity, short average path lengths (small-world property) and right-skewed but rarely power law degree distributions. On the other hand homophily, defined as the propensity of similar agents to connect to each other, is one of the most fundamental social processes observed in many human and animal societies. In this paper we examine the extent to which homophily is sufficient to produce the typical structural properties of social networks. To do so, we conduct a simulation study based on the Social Distance Attachment (SDA) model, a particular kind of Random Geometric Graph (RGG), in which nodes are embedded in a social space and connection probabilities depend functionally on distances between nodes. We derive the form of the model from first principles based on existing analytical results and argue that the mathematical construction of RGGs corresponds directly to the homophily principle, so they provide a good model for it. We find that homophily, especially when combined with a random edge rewiring, is sufficient to reproduce many of the characteristic features of social networks. Additionally, we devise a hybrid model combining SDA with the configuration model that allows generating homophilic networks with arbitrary degree sequences and we use it to study interactions of homophily with processes imposing constraints on degree distributions. We show that the effects of homophily on clustering are robust with respect to distribution constraints, while degree assortativity can be highly dependent on the particular kind of enforced degree sequence.

Read Full Article at RT.com

Ubiquiti Networks UniFi Cloud Key with firmware versions 0.5.9 and 0.6.0 suffer from weak crypto, privilege escalation, and command injection vulnerabilities.

This whitepaper goes into detail on design and implementation details for performing voice encryption on telephone networks. Written in Spanish.

This toolkit houses various IPv6 tools that have been tested to compile and run on Debian GNU/Linux 6.0, FreeBSD 9.0, NetBSD 5.1, OpenBSD 5.0, Mac OS 10.8.0, and Ubuntu 11.10.

This toolkit houses various IPv6 tools that have been tested to compile and run on Debian GNU/Linux 6.0, FreeBSD 9.0, NetBSD 5.1, OpenBSD 5.0, Mac OS 10.8.0, and Ubuntu 11.10.

This toolkit houses various IPv6 tools that have been tested to compile and run on Debian GNU/Linux 6.0, FreeBSD 9.0, NetBSD 5.1, OpenBSD 5.0, Mac OS 10.8.0, and Ubuntu 11.10.

Extreme Networks Aerohive HiveOS versions 11.x and below remote denial of service exploit. An unauthenticated malicious user can trigger a denial of service (DoS) attack when sending specific application layer packets towards the Aerohive NetConfig UI. This proof of concept exploit renders the application unusable for 305 seconds or 5 minutes with a single HTTP request using the action.php5 script calling the CliWindow function thru the _page parameter, denying access to the web server hive user interface.

Two chance encounters propelled remote direct memory access from a good but obscure idea for fast networks into the jet fuel for the world’s more powerful supercomputers. The lucky breaks launched the fortunes of an Israel-based startup that staked its fortunes on InfiniBand, a network based on RDMA. Later, that startup, Mellanox Technologies, helped steer Read article >

The post How RDMA Became the Fuel for Fast Networks appeared first on The Official NVIDIA Blog.