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.

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.

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.

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.

Unveiling network-based functional features through integration of gene expression into protein networks.

Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers.

Liver fibrosis alleviation after co-transplantation of hematopoietic stem cells with mesenchymal stem cells in patients with thalassemia major.

The aims of this study are to determine the replacement rate of damaged hepatocytes by donor-derived cells in sex-mismatched recipient patients with thalassemia major and to determine whether co-transplantation of mesenchymal stem cells and hematopoietic stem cells (HSCs) can alleviate liver fibrosis. Ten sex-mismatched donor-recipient pairs who received co-transplantation of HSCs with mesenchymal stem cells were included in our study. Liver biopsy was performed before transplantation. Two other liver biopsies were performed between 2 and 5 years after transplantation. The specimens were studied for the presence of donor-derived epithelial cells or hepatocytes using fluorescence in situ hybridization by X- and Y-centromeric probes and immunohistochemical staining for pancytokeratin, CD45, and a hepatocyte-specific antigen. All sex-mismatched tissue samples demonstrated donor-derived hepatocyte independent of donor gender. XY-positive epithelial cells or hepatocytes accounted for 11 to 25% of the cells in histologic sections of female recipients in the first follow-up. It rose to 47-95% in the second follow-up. Although not statistically significant, four out of ten patients showed signs of improvement in liver fibrosis. Our results showed that co-transplantation of HSC with mesenchymal stem cells increases the rate of replacement of recipient hepatocytes by donor-derived cells and may improve liver fibrosis.

Efficacy of hepatic T2* MRI values and serum ferritin concentration in predicting thalassemia major classification for hematopoietic stem cell transplantation.

Liver biopsy has been performed for many decades for classifying the patients with TM. Meanwhile, using non-invasive methods such as T2* MRI technique has been recently much more considered to determine the hepatic iron overload. Ninety-three pediatric HSCT candidates with TM who underwent liver biopsy were included in this study. Hepatic T2* MRI values and serum ferritin concentrations were assessed to investigate and determine the useful method in detection of patients with TM class III whom received different conditioning regimens, in comparison with class I and II. Twenty (21.5%) patients were categorized as class III. Hepatic T2* MRI could detect TM class III patients with 60% sensitivity and 87.67% specificity (LR+: 4.867, accuracy: 81.72%), while predictive feature of ferritin values for distinguishing patients with TM class III was not statistically significant (p-value >0.01). Combination of T2*MRI with age (T2*-age) could detect TM class III with 85% sensitivity and 72.6% specificity (LR+: 3.1, accuracy: 75.27%).T2*-age may be considered as an alternative and non-invasive method to liver biopsy for differentiation and classification of patients with TM before transplantation.

Discovering dominant pathways and signal-response relationships in signaling networks through nonparametric approaches.

A signaling pathway is a sequence of proteins and passenger molecules that transmits information from the cell surface to target molecules. Understanding signal transduction process requires detailed description of the involved pathways. Several methods and tools resolved this problem by incorporating genomic and proteomic data. However, the difficulty of obtaining prior knowledge of complex signaling networks limited the applicability of these tools. In this study, based on the simulation of signal flow in signaling network, we introduce a method for determining dominant pathways and signal response to stimulations. The model uses topology-weighted transit compartment approach and comprises four main steps which include weighting the edges, simulating signal transduction in the network (weighting the nodes), finding paths between initial and target nodes, and assigning a significance score to each path. We applied the proposed model to eighty-three signaling networks by using biologically derived source and sink molecules. The recovered dominant paths matched many known signaling pathways and suggesting a promising index to analyze the phenotype essentiality of molecule encoding paths. We also modeled the stimulus-response relations in long and short-term synaptic plasticity based on the dominant signaling pathway concept. We showed that the proposed method not only accurately determines dominant signaling pathways, but also identifies effective points of intervention in signal transduction.

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.

Non-TBI hematopoietic stem cell transplantation in pediatric AML patients: a single-center experience.

BACKGROUND: Hematopoietic stem cell transplantation (HSCT) has been established as a promising treatment in acute myeloid leukaemia (AML). Several studies have been performed to minimize the toxicity of HSCT in children without impairing the efficacy. We report our long-term results of HSCT in pediatric AML patients using non-total body irradiation conditioning regimen. PROCEDURE: From May 1991 to June 2010, 133 pediatric patients with AML (age<15 y) who were referred to our institute underwent autologous (auto-) or allogeneic (allo-) HSCT. The conditioning regimen consisted of oral busulfan plus etoposide in auto-HSCT patients and oral busulfan plus cyclophosphamide in allo-HSCT patients. RESULTS: Overall survival (OS), leukemia-free survival (LFS), probability of relapse, and transplantation-related mortality at 3 years were 67.6%, 62.2.5%, 27.3%, and 10.1%, respectively. There was no significant difference between allo-HSCT and auto-HSCT groups. In multivariable analysis using Cox proportional hazards regression model, male sex was associated with significantly improved OS (P<0.001) and LFS (P=0.022). An age ≤3 years was associated with higher relapse (P=0.034) and worse OS (P=0.001) and LFS (P=0.014). CONCLUSIONS: The role of allo-HSCT in pediatric AML patients in first complete remission is uncertain. Further randomized studies are recommended to clarify the optimal postremission therapy in these patients.

Spike phase synchronization in delayed-coupled neural networks: uniform vs. non-uniform transmission delay.

In this paper, we investigated phase synchronization in delayed dynamical networks. Non-identical spiking Hindmarsh-Rose neurons were considered as individual dynamical systems and coupled through a number of network structures such as scale-free, Erdos-Rényi, and modular. The individual neurons were coupled through excitatory chemical synapses with uniform or distributed time delays. The profile of spike phase synchrony was different when the delay was uniform across the edges as compared to the case when it was distributed, i.e., different delays for the edges. When an identical transmission delay was considered, a quasi-periodic pattern was observed in the spike phase synchrony. There were specific values of delay where the phase synchronization reached to its peaks. The behavior of the phase synchronization in the networks with non-uniform delays was different with the former case, where the phase synchrony decreased as distributed delays introduced to the networks.

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.

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.

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.

Long-term follow-up of children treated with daclizumab for steroid-refractory gastrointestinal GvHD in a prospective study.

Daclizumab, a humanized MoAB to IL-2Ra, has been found to be safe and effective in adults with refractory GvHD; however, data in children are limited. The aim of this prospective study was to evaluate the long-term safety and efficacy of daclizumab in children with steroid-refractory GI aGvHD. This study included 13 children who developed steroid-refractory GI GvHD between 2007 and 2009. When first-line treatment failed, daclizumab was given in a regimen of 1 mg/kg intravenously and then repeated on a 10- to 14-day interval for maximum five doses if necessary. Daclizumab was well tolerated, but infections were common. Ten patients responded to daclizumab completely, one patient responded partially, and two patients failed to respond. With a median follow-up of 630 days, 10 patients were alive and free of severe infections, but among them, four patients were suffering from cGvHD. Of the three remaining patients, one died because of bacterial meningitis, and the other two patients died because of severe refractory GI GvHD. This long-term evaluation showed that daclizumab could be an effective and relatively safe treatment in most of the pediatric patients with severe steroid-refractory GI GvHD.

The outcome of allogeneic hematopoietic stem cell transplants without total body irradiation in pediatric patients with acute lymphoblastic leukemia: single centre experience.

The most widely accepted conditioning regimen to allogeneic hematopoietic stem cell transplantation consists of total body irradiation, especially in patients affected by acute lymphoblastic leukemia (ALL). In this retrospective study, we report our experience on hematopoietic stem cell transplantation in 44 pediatric patients with acute lymphoblastic leukemia using a non-radiation-based conditioning regimen (busulfan/cyclophosphamide). Median age at transplantation was 12.5 years (range, 4 to 14 y). 39 out of 44 patients received transplants in complete remission. At a median follow-up of 390 days, the probabilities of 3-year disease-free survival and overall survival were 50% and 68%, respectively. Disease status of hematopoietic stem cell transplantation was the only significant variable affecting the overall survival. Acute and chronic graft-versus-host disease occurred in 23 (64%) and 12(18%) patients, respectively. Relapse was significantly higher among patients transplanted in advanced disease status. The results of the study indicate that non-radiation-based preparative regimens can be used in pediatric patients with ALL. However, well-designed comparative trials are needed to better clarify the difference between radiation and non-radiation-based conditioning regimens in pediatric ALL.

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.

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.

Psychogenic seizures and frontal disconnection: EEG synchronisation study.

OBJECTIVE: Psychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients. METHODS: The authors analysed the whole-head surface topography of multivariate phase synchronisation (MPS) in interictal high-density EEG of 13 PNES patients as compared with 13 age- and sex-matched controls. MPS mapping reduces the wealth of dynamic data obtained from high-density EEG to easily readable synchronisation maps, which provide an unbiased overview of any changes in functional connectivity associated with distributed cortical abnormalities. The authors computed MPS maps for both Laplacian and common-average-reference EEGs. RESULTS: In a between-group comparison, only patchy, non-uniform changes in MPS survived conservative statistical testing. However, against the background of these unimpressive group results, the authors found widespread inverse correlations between individual PNES frequency and MPS within the prefrontal and parietal cortices. INTERPRETATION: PNES appears to be associated with decreased prefrontal and parietal synchronisation, possibly reflecting dysfunction of networks within these regions.