Byzantine-Resilient Second-Order Consensus in Networked Systems.

This article studies the second-order consensus problem in networked systems containing the so-called Byzantine misbehaving nodes when only an upper bound on either the local or the total number of misbehaving nodes is known. The existing results on this subject are limited to malicious/faulty model of misbehavior. Moreover, existing results consider consensus among normal nodes in only one of the two states, with the other state converging to either zero or a predefined value. In this article, a distributed control algorithm capable of withstanding both locally bounded and totally bounded Byzantine misbehavior is proposed. When employing the proposed algorithm, the normal nodes use a combination of the two relative state values obtained from their neighboring nodes to decide which neighbors should be ignored. By introducing an underlying virtual network, conditions on the robustness of the communication network topology for consensus on both states are established. Numerical simulation results are presented to illustrate the effectiveness of the proposed control algorithm.

DyVGRNN: DYnamic mixture Variational Graph Recurrent Neural Networks.

Although graph representation learning has been studied extensively in static graph settings, dynamic graphs are less investigated in this context. This paper proposes a novel integrated variational framework called DYnamic mixture Variational Graph Recurrent Neural Networks (DyVGRNN), which consists of extra latent random variables in structural and temporal modelling. Our proposed framework comprises an integration of Variational Graph Auto-Encoder (VGAE) and Graph Recurrent Neural Network (GRNN) by exploiting a novel attention mechanism. The Gaussian Mixture Model (GMM) and the VGAE framework are combined in DyVGRNN to model the multimodal nature of data, which enhances performance. To consider the significance of time steps, our proposed method incorporates an attention-based module. The experimental results demonstrate that our method greatly outperforms state-of-the-art dynamic graph representation learning methods in terms of link prediction and clustering.2.

Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.

Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others. The key to successful implementation of such integration algorithms lies in the optimal selection of parameters, and thresholding is a crucial component in this process. To improve the predictive accuracy of context-specific models, we introduce a new integration framework that improves the ranking of related genes and homogenizes the expression values of those gene sets using single-sample Gene Set Enrichment Analysis (ssGSEA). In this study, we coupled ssGSEA with GIMME and validated the advantages of the proposed framework to predict the ethanol formation of yeast grown in the glucose-limited chemostats, and to simulate metabolic behaviors of yeast growth in four different carbon sources. This framework enhances the predictive accuracy of GIMME which we demonstrate for predicting the yeast physiology in nutrient-limited cultures.

Control and Optimisation of Power Grids Using Smart Meter Data: A Review.

This paper provides a comprehensive review of the applications of smart meters in the control and optimisation of power grids to support a smooth energy transition towards the renewable energy future. The smart grids become more complicated due to the presence of small-scale low inertia generators and the implementation of electric vehicles (EVs), which are mainly based on intermittent and variable renewable energy resources. Optimal and reliable operation of this environment using conventional model-based approaches is very difficult. Advancements in measurement and communication technologies have brought the opportunity of collecting temporal or real-time data from prosumers through Advanced Metering Infrastructure (AMI). Smart metering brings the potential of applying data-driven algorithms for different power system operations and planning services, such as infrastructure sizing and upgrade and generation forecasting. It can also be used for demand-side management, especially in the presence of new technologies such as EVs, 5G/6G networks and cloud computing. These algorithms face privacy-preserving and cybersecurity challenges that need to be well addressed. This article surveys the state-of-the-art of each of these topics, reviewing applications, challenges and opportunities of using smart meters to address them. It also stipulates the challenges that smart grids present to smart meters and the benefits that smart meters can bring to smart grids. Furthermore, the paper is concluded with some expected future directions and potential research questions for smart meters, smart grids and their interplay.

Learning Graph Representations With Maximal Cliques.

Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional networks (GCNs) can be regarded as one of the successful approaches to classification tasks on graph data, although the structure of this approach limits its performance. In this work, a novel representation learning approach is introduced based on spectral convolutions on graph-structured data in a semisupervised learning setting. Our proposed method, COnvOlving cLiques (COOL), is constructed as a neighborhood aggregation approach for learning node representations using established GCN architectures. This approach relies on aggregating local information by finding maximal cliques. Unlike the existing graph neural networks which follow a traditional neighborhood averaging scheme, COOL allows for aggregation of densely connected neighboring nodes of potentially differing locality. This leads to substantial improvements on multiple transductive node classification tasks.

Online spike sorting via deep contractive autoencoder.

Spike sorting - the process of separating spikes from different neurons - is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neuron's activity from background electrical noise based on the shapes of the waveforms obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (CAE) architecture. CAEs are neural networks that can learn a latent state representation of their inputs. The main advantage of CAE-based approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our deep CAE-based online spike sorting algorithm achieves over 90% accuracy in sorting unseen spike waveforms, outperforming existing models and maintaining a performance close to the offline case. In the offline scenario, our method substantially outperforms the existing models, providing an average improvement of 40% in accuracy over different datasets.

Outcome of Peripheral Blood Allogeneic Hematopoietic Stem Cell Transplantation as a Treatment Option in Patients With Severe Aplastic Anemia Between 40 and 50 Years.

The frontline treatment for patients younger than 40 years with severe aplastic anemia (AA) is allogeneic hematopoietic stem cell transplantation (HSCT) from a human leukocyte antigen-identical sibling donor. However, in patients with severe AA who are older than 40 years, allogeneic HSCT has been found to be associated with increased treatment-related mortality and toxicity, even when matched sibling donors are used. We report our institutional experience with allogeneic HSCT in patients with severe AA between 40 and 50 years. A total of 19 patients with severe AA were included in the study. Overall survival (OS) and disease-free survival (DFS) were estimated using the Kaplan-Meier method. The mean age of patients at the time of transplant was 43.79 years, and 57.9% were male. The mortality rate was 36.8%, attributed to infection (10.5%), relapse (15.8%), and renal failure (5.3%) in all cases. Acute graft-versus-host disease (GVHD) occurred in five patients (26.3%), and chronic GVHD occurred in two patients (10.5%). The 5-year OS was 62% and the 5-year DFS was 52%. We found that the patient's age, platelet level prior to transplantation, and the number of CD3 cells infused for each transplant were independent prognostic factors for OS, and the age and sex of the patient, graft rejection, and platelet level prior to transplantation were significant prognostic factors associated with DFS. We recommend that immunosuppressive therapy be considered as a first-line treatment in patients with severe AA who are older than 40 years. Allogeneic HSCT can be considered a valid alternative option in patients whose suppression therapy fails.

A Metabolic Model of Intestinal Secretions: The Link between Human Microbiota and Colorectal Cancer Progression.

The human gut microbiota plays a dual key role in maintaining human health or inducing disorders, for example, obesity, type 2 diabetes, and cancers such as colorectal cancer (CRC). High-throughput data analysis, such as metagenomics and metabolomics, have shown the diverse effects of alterations in dynamic bacterial populations on the initiation and progression of colorectal cancer. However, it is well established that microbiome and human cells constantly influence each other, so it is not appropriate to study them independently. Genome-scale metabolic modeling is a well-established mathematical framework that describes the dynamic behavior of these two axes at the system level. In this study, we created community microbiome models of three conditions during colorectal cancer progression, including carcinoma, adenoma and health status, and showed how changes in the microbial population influence intestinal secretions. Conclusively, our findings showed that alterations in the gut microbiome might provoke mutations and transform adenomas into carcinomas. These alterations include the secretion of mutagenic metabolites such as H2S, NO compounds, spermidine and TMA (trimethylamine), as well as the reduction of butyrate. Furthermore, we found that the colorectal cancer microbiome can promote inflammation, cancer progression (e.g., angiogenesis) and cancer prevention (e.g., apoptosis) by increasing and decreasing certain metabolites such as histamine, glutamine and pyruvate. Thus, modulating the gut microbiome could be a promising strategy for the prevention and treatment of CRC.

Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models.

Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results.

Prevalence of Sexual Dysfunction and Related Risk Factors in Men with Multiple Sclerosis in Iran: A Multicenter Study.

INTRODUCTION: Sexual dysfunction (SD) is a common complaint in patients with multiple sclerosis (MS). The aim of this study was to assess the prevalence of SD and its related risk factors in men with MS in Iran. METHODS: In this cross-sectional study, 320 men who had been diagnosed with MS according to the McDonald revised criteria were recruited from January to June 2019, from the north, south, east, west, and central parts of Iran. Patients were assessed using the Male Sexual Health Questionnaire (MSHQ), International Index of Erectile Function (IIEF), The Multiple Sclerosis Intimacy and Sexuality Questionnaire-(MSISQ 19), Sexual Quality of Life-Men (SQOL-M), and Standard General Health Questionnaire (GHQ). RESULTS: Sexual dysfunction, defined as total IIEF score ≤ 45 was present in 114 patients (35.6%). The results of univariate logistic regression showed that there were significant direct relations between age (OR 1.050, 95% CI 1.02-1.08), Expanded Disability Status Scale (EDSS) (OR 1.45, 95% CI 1.24-1.7), duration of MS (OR 1.005, 95% CI 1.002-1.009), MSISQ-19 (OR 1.103, 95% CI 1.078-1.128), GHQ (OR 1.04, 95% CI 1.03-1.06), SQOL-M (OR 0.930, 95% CI 0.914-0.947), smoking (OR 1.941, 95% CI 1.181-3.188), non-MS chronic disease (OR 1.91, 95% CI 1.20-3.04), having a main sexual partner (OR 2.56, 95% CI 1.32-4.94), and significant inverse relations between exercise (OR 0.584, 95% CI 0.364-0.936) and regular sexual activity (OR 0.241, 95% CI 0.15-0.40), with the prevalence of SD. The results of multiple logistic regression indicated that the age, MSISQ-19, and SQOL-M were the only independent predictive factors for SD in these patients. CONCLUSION: The prevalence of SD in men with MS in Iran is relatively high. These patients should be screened, diagnosed, and treated for SD and influencing factors.

Percolation of heterogeneous flows uncovers the bottlenecks of infrastructure networks.

Whether it be the passengers' mobility demand in transportation systems, or the consumers' energy demand in power grids, the primary purpose of many infrastructure networks is to best serve this flow demand. In reality, the volume of flow demand fluctuates unevenly across complex networks while simultaneously being hindered by some form of congestion or overload. Nevertheless, there is little known about how the heterogeneity of flow demand influences the network flow dynamics under congestion. To explore this, we introduce a percolation-based network analysis framework underpinned by flow heterogeneity. Thereby, we theoretically identify bottleneck links with guaranteed decisive impact on how flows are passed through the network. The effectiveness of the framework is demonstrated on large-scale real transportation networks, where mitigating the congestion on a small fraction of the links identified as bottlenecks results in a significant network improvement.

Optimization of Communication Network Topology in Distributed Control Systems Subject to Prescribed Decay Rate.

In this paper, we propose a simple cohesive framework to find an optimal directed control network topology with minimum number of links while a prescribed decay rate is satisfied in the transient response of a distributed control system. In order to guarantee the system's decay rate to be faster than a prespecified value, a constraint on the dominant eigenvalue of the system is required to be considered. This results in a nonconvex optimization problem as eigenvalue of a parametric nonsymmetric matrix is a nonconvex, nonsmooth, and even non-Lipschitz function. Here, we present a convex equivalent optimization problem whose minimizer also solves this eigenvalue optimization problem. This optimization problem proposes a state-feedback matrix which results in a decay rate faster than a given value while input signal costs are considered. The equivalent optimization problem in combination with sparsity-promoting optimal control constitutes a combinatorial optimization problem. Using alternating direction method of multipliers, the problem is decomposed into a chain of analytically solvable subproblems which are differentiable and separable. The proposed optimization framework includes relative preference between the topology of the control network and the decay rate of the system. The simulation results show the effectiveness of the proposed framework.

Appropriate time to apply control input to complex dynamical systems.

Controlling a network structure has many potential applications many fields. In order to have an effective network control, not only finding good driver nodes is important, but also finding the optimal time to apply the external control signals to network nodes has a critical role. If applied in an appropriate time, one might be to control a network with a smaller control signals, and thus less energy. In this manuscript, we show that there is a relationship between the strength of the internal fluxes and the effectiveness of the external control signal. To be more effective, external control signals should be applied when the strength of the internal states is the smallest. We validate this claim on synthetic networks as well as a number of real networks. Our results may have important implications in systems medicine, in order to find the most appropriate time to inject drugs as a signal to control diseases.

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures.

OBJECTIVES: Psychogenic non-epileptic seizures (PNES) have been hypothesized to emerge in the context of neural networks instability. To explore this hypothesis in children, we applied a graph theory approach to examine connectivity in neural networks in the resting-state EEG in 35 children with PNES, 31 children with other functional neurological symptoms (but no PNES), and 75 healthy controls. METHODS: The networks were extracted from Laplacian-transformed time series by a coherence connectivity estimation method. RESULTS: Children with PNES (vs. controls) showed widespread changes in network metrics: increased global efficiency (gamma and beta bands), increased local efficiency (gamma band), and increased modularity (gamma and alpha bands). Compared to controls, they also had higher levels of autonomic arousal (e.g., lower heart variability); more anxiety, depression, and stress on the Depression Anxiety and Stress Scales; and more adverse childhood experiences on the Early Life Stress Questionnaire. Increases in network metrics correlated with arousal. Children with other functional neurological symptoms (but no PNES) showed scattered and less pronounced changes in network metrics. CONCLUSION: The results indicate that children with PNES present with increased activation of neural networks coupled with increased physiological arousal. While this shift in functional organization may confer a short-term adaptive advantage-one that facilitates neural communication and the child's capacity to respond self-protectively in the face of stressful life events-it may also have a significant biological cost. It may predispose the child's neural networks to periods of instability-presenting clinically as PNES-when the neural networks are faced with perturbations in energy flow or with additional demands.

Target controllability with minimal mediators in complex biological networks.

Controllability of a complex network system is related to finding a set of minimum number of nodes, known as drivers, controlling which allows having a full control on the dynamics of the network. For some applications, only a portion of the network is required to be controlled, for which target control has been proposed. Often, along the controlling route from driver nodes to target nodes, some mediators (intermediate nodes) are also unwillingly controlled, which might cause various side effects. In controlling cancerous cells, unwillingly controlling healthy cells, might result in weakening them, thus affecting the immune system against cancer. This manuscript proposes a suitable candidate solution to the problem of finding minimum number of driver nodes under minimal mediators. Although many others have attempted to develop algorithms to find minimum number of drivers for target control, the newly proposed algorithm is the first one that is capable of achieving this goal and at the same time, keeping the number of the mediators to a minimum. The proposed controllability condition, based on path lengths between node pairs, meets Kalman's controllability rank condition and can be applied on directed networks. Our results show that the path length is a major determinant of in properties of the target control under minimal mediators. As the average path length becomes larger, the ratio of drivers to target nodes decreases and the ratio of mediators to targets increases. The proposed methodology has potential applications in biological networks. The source code of the algorithm and the networks that have been used are available from the following link: https://github.com/LBBSoft/Target-Control-with-Minimal-Mediators.git.

An alternative test for determining autologous stem cell transplantation cell harvesting outcome: Comparison between predictive values of two tests.

Daily CD34+ cells enumeration as a success indicator of stem cell pheresis procedure using flow cytometry is costly, lengthy, and labor-intensive. Thus, finding a simpler method to achieve the optimum time for harvesting the minimum required stem cells for transplantation could be helpful. The aim of this study was to evaluate the predictive value of reticulocytes fractions and their sensesivity and specificity in guiding CD34+ cell harvesting by G-CSF mobilization strategy. In this study, 49 candidates for autologous peripheral blood stem cell transplantation were enrolled. Before leukapheresis, the immature reticulocytes fraction (IRF) and CD34+ cell count were measured. Moreover, patients were evaluated for leukapheresis outcomes in two MNC and cMNC groups. Here we demonstrated that IRF, LFR, and MFR with the associated criterion of >17.3, ≤82.5, and >15.9, respectively, earned 100 % specificity and 47.2 %, 47.22 %, and 41.46 % sensitivity to predict the minimum required CD34+ cell count. Furthermore, IRF-V (Value) and MFR-V with the associated criterion of >0.77 and >0.55, respectively, earned 58.33 %, 66.67 % sensitivity and 84.62 %, 69.23 % of specificity, separately. As only MFR-V was able to predict the platelet engraftment (P-value = 0.014), none of the other above mentioned factors were not able to predict the neutrophil engraftment. Likewise, it was shown that patients who underwent MNC leukapheresis had a statistically significantly higher total WBC, harvested CD34+ cells, MNCs/ kg, and lower apheresis durations (P-values<0.05). Taken together, using IRF and its maturity stages seems to be a compelling predictor of minimal required CD34+ cells in autologous peripheral blood stem cell transplantation.

GEMtractor: extracting views into genome-scale metabolic models.

SUMMARY: Computational metabolic models typically encode for graphs of species, reactions and enzymes. Comparing genome-scale models through topological analysis of multipartite graphs is challenging. However, in many practical cases it is not necessary to compare the full networks. The GEMtractor is a web-based tool to trim models encoded in SBML. It can be used to extract subnetworks, for example focusing on reaction- and enzyme-centric views into the model. AVAILABILITY AND IMPLEMENTATION: The GEMtractor is licensed under the terms of GPLv3 and developed at github.com/binfalse/GEMtractor-a public version is available at sbi.uni-rostock.de/gemtractor.

Robust Second-Order Consensus Using a Fixed-Time Convergent Sliding Surface in Multiagent Systems.

Faster convergence is always sought in many applications. Designing fixed-time control has recently gained much attention since, for this type of control structure, the convergence time of the states does not depend on initial conditions, unlike other control methods providing faster convergence. This paper proposes a new distributed algorithm for second-order consensus in multiagent systems by using a full-order fixed-time convergent sliding surface. The stability analysis is performed using the Lyapunov function and bi-homogenous property. Moreover, the proposed control is smooth and free from any singularity. The robustness of the proposed scheme is verified both in the presence of Lipschitz disturbances and uncertainties in the network. The proposed method is compared with a state-of-the-art method to show the effectiveness.

Application of hyperbolic geometry in link prediction of multiplex networks.

Recently multilayer networks are introduced to model real systems. In these models the individuals make connection in multiple layers. Transportation networks, biological systems and social networks are some examples of multilayer networks. There are various link prediction algorithms for single-layer networks and some of them have been recently extended to multilayer networks. In this manuscript, we propose a new link prediction algorithm for multiplex networks using two novel similarity metrics based on the hyperbolic distance of node pairs. We use the proposed methods to predict spurious and missing links in multiplex networks. Missing links are those links that may appear in the future evolution of the network, while spurious links are the existing connections that are unlikely to appear if the network is evolving normally. One may interpret spurious links as abnormal links in the network. We apply the proposed algorithm on real-world multiplex networks and the numerical simulations reveal its superiority than the state-of-the-art algorithms.

Peripheral blood stem cell apheresis in low-weight children: A single centre study.

Peripheral blood stem cell transplantation (PBSCT) is now widely used in both malignant and non-malignant hematologic diseases as a treatment strategy. Using this approach, a controversial group of donors is children weighing 20 kg or less. The aim of this study was to evaluate results of allogeneic and autologous PBSCT and also the efficacy of our suggested alternative method for a custom prime in cell harvesting of this group. All the participants' demographic and laboratory data were collected before apheresis. A total of 37 individuals participated in this study of which 12 and 25 of them were categorized in autologous and allogeneic groups respectively. For the apheresis procedure, a central venous access was used as well as the custom prime method with some changes. Apheresis details, as well as CD34 and CD3 cell counts in the allogeneic and autologous groups, were calculated. In this study, 91.9% (N = 34) of all individuals achieved the minimal amount of cells for PBSCT (2 × 106 CD34+ cells/kg) in one session. On the other hand, 12% (N = 3) of donors in the allogeneic group achieved the minimal threshold in 2 apheresis sessions. During the leukapheresis a total processed blood volume/total blood volume ratio (TPBV/TBV) was calculated as 4.64 ± 1.06 and 5.18 ± 0.73 fold in the allogeneic and autologous groups respectively. The mean of harvested CD34 cells in allogeneic and autologous groups was 5.28 ± 3.47 × 106 and 3.57 ± 2.9 × 106 cells/kg respectively. Likewise, in the allogeneic group, the mean of the harvested CD3 cell count was 339 ± 141 × 106/kg. Also, the median day of white blood cell (WBC) engraftment was 14 and 13 for allogeneic and autologous groups respectively. Furthermore, the median day of platelet engraftment was 19.5 for both allogeneic and autologous groups. Among the recipients of the allogeneic group, acute graft versus host disease (aGVHD) was detected in 56% (N = 14) of patients and this was also correct for chronic GVHD. Taken together, it was shown, despite the probable complications of peripheral blood stem cell apheresis in donors weighing less than 20 kg; that it is possible to perform this procedure without any complication during the leukapheresis.