Optimal plug‐in hybrid electric vehicle performance management using decentralized multichannel network design

In addition to providing mobility, plug-in hybrid electric vehicles (PHEVs) provide a two-sided energy exchange opportunity which makes them highly flexible distributed energy storage systems for the future of energy systems. This paper analyzes PHEVs' performance from the perspective of urban traffic and energy using a decentralized multichannel blockchain network based on the hyperledger model. This network using a layered design and local management of energy sources can significantly contribute to urban management and optimal use of its infrastructures. Then, dynamic modelling of PHEVs in this network is performed, and their data is added to the network to evaluate the network performance compared with the current centralized networks. The results indicated that the proposed blockchain network could simultaneously optimize PHEVs' performance, urban traffic management, and energy systems. Furthermore, by utilizing smart contracts, it can consider and optimize multiple challenges, such as congestion in the electricity network, urban traffic, and limited fuel, simultaneously. Therefore, it gives a strong tool to study the impact of mass deployment of PHEVs and their value and role in the sustainable cities and communities of the future while helping to support the global efforts toward affordable and clean energy for all

A decentralized blockchain-based energy market for citizen energy communities

Despite the fact that power grids have been planned and utilized using centralized networks for many years, there are now significant changes occurring as a result of the growing number of distributed energy resources, the development of energy storage systems and devices, and the increased use of electric vehicles. In light of this development, it is pertinent to ask what an efficient approach would be to the operation and management of future distribution grids consisting of millions of distributed and even mobile energy elements. Parallel to this evolution in power grids, there has been rapid growth in decentralized management technology due to the development of relevant technologies such as blockchain networks. Blockchain is an advanced technology that enables us to answer the question raised above. This paper introduces a decentralized blockchain network based on the Hyperledger Fabric framework. The proposed framework enables the formation of local energy markets of future citizen energy communities (CECs) through peer-to-peer transactions. In addition, it is designed to ensure adequate load supply and observe the network’s constraints while running an optimal operation point by consensus among all of the players in a CEC. An open-source tool in Python is used to verify the performance of the proposed framework and compare the results. Through its distributed and layered management structure, the proposed blockchain-based framework proves its superior flexibility and proper functioning. Moreover, the results show that the proposed model increases system performance, reduces costs, and reaches an operating point based on consensus among the microgrid elements

Sustainable and inclusive demand-side resilience: a semi-dynamic model for outage costs

The power system is primarily designed and concerned with supplying electricity to its customers at all times. Nevertheless, power outages are inevitable; therefore, one of the challenges is to accurately determine the costs and damages to consumers in a fair and inclusive manner. Outage events are regularly costed based on a parameter called the Value of Lost Load (VoLL/VOLL). Although some of the influencing factors on outage costs have been identified in the literature, the exact determination of the damage to customers is still considered a big challenge. This work is an effort toward a more sustainable and inclusive demand-side resilience that provides a semi-dynamic model for the assignment of the power outage damage costs to the customers. The results of the proposed method show how using a semi-dynamic model for outage costs leads to more sustainable and inclusive operating decisions in the power system while also leads to a fairer allocation of costs

A stochastic framework for secure reconfiguration of active distribution networks

Automatic reconfiguration is one of the key actions in self-healing distribution networks. In these networks, after detecting and isolating the faulted portion, an automatic reconfiguration procedure is performed to restore the maximum possible affected loads without further interruptions during repair operations. This procedure becomes more complicated in the networks with integrated distributed generation units as they can bring security challenges for the reconfigured network after a fault event. To overcome these challenges, a stochastic framework is proposed here. In this framework, the reconfiguration procedure is conducted with a fast and reliable method which is based on the graph theory. Besides, the security challenges of utilizing distributed generations after an event are highlighted. Then, since a faulted network is more prone to subsequent faults, different actions of changing the distribution generations output power, preventing the insecure increment of short circuit capacity, and also considering the loadability improvement are proposed in the reconfiguration framework. Then in the final stage, the vulnerability of the distribution system to the uncertainties of load demand is resolved through a chance-constrained programming-based approach. To see the performance of the proposed stochastic framework, it is tested on a standard test system and the results prove its goodness and applicability for real distribution networks

Pharmacological screening using an FXN-EGFP cellular genomic reporter assay for the therapy of Friedreich ataxia

Copyright @ 2013 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Friedreich ataxia (FRDA) is an autosomal recessive disorder characterized by neurodegeneration and cardiomyopathy. The presence of a GAA trinucleotide repeat expansion in the first intron of the FXN gene results in the inhibition of gene expression and an insufficiency of the mitochondrial protein frataxin. There is a correlation between expansion length, the amount of residual frataxin and the severity of disease. As the coding sequence is unaltered, pharmacological up-regulation of FXN expression may restore frataxin to therapeutic levels. To facilitate screening of compounds that modulate FXN expression in a physiologically relevant manner, we established a cellular genomic reporter assay consisting of a stable human cell line containing an FXN-EGFP fusion construct, in which the EGFP gene is fused in-frame with the entire normal human FXN gene present on a BAC clone. The cell line was used to establish a fluorometric cellular assay for use in high throughput screening (HTS) procedures. A small chemical library containing FDA-approved compounds and natural extracts was screened and analyzed. Compound hits identified by HTS were further evaluated by flow cytometry in the cellular genomic reporter assay. The effects on FXN mRNA and frataxin protein levels were measured in lymphoblast and fibroblast cell lines derived from individuals with FRDA and in a humanized GAA repeat expansion mouse model of FRDA. Compounds that were established to increase FXN gene expression and frataxin levels included several anti-cancer agents, the iron-chelator deferiprone and the phytoalexin resveratrol.Muscular Dystrophy Association (USA), the National Health and Medical Research Council (Australia), the Friedreich’s Ataxia Research Alliance (USA), the Brockhoff Foundation (Australia), the Friedreich Ataxia Research Association (Australasia), Seek A Miracle (USA) and the Victorian Government’s Operational Infrastructure Support Program

Metastable Atrial State Underlies the Primary Genetic Substrate for MYL4 Mutation-Associated Atrial Fibrillation

Background:Atrial fibrillation (AF) is the most common clinical arrhythmia and is associated with heart failure, stroke, and increased mortality. The myocardial substrate for AF is poorly understood because of limited access to primary human tissue and mechanistic questions around existing in vitro or in vivo models.Methods:Using an MYH6:mCherry knock-in reporter line, we developed a protocol to generate and highly purify human pluripotent stem cell–derived cardiomyocytes displaying physiological and molecular characteristics of atrial cells. We modeled human MYL4 mutants, one of the few definitive genetic causes of AF. To explore non–cell-autonomous components of AF substrate, we also created a zebrafish Myl4 knockout model, which exhibited molecular, cellular, and physiologic abnormalities that parallel those in humans bearing the cognate mutations.Results:There was evidence of increased retinoic acid signaling in both human embryonic stem cells and zebrafish mutant models, as well as abnormal expression and localization of cytoskeletal proteins, and loss of intracellular nicotinamide adenine dinucleotide and nicotinamide adenine dinucleotide + hydrogen. To identify potentially druggable proximate mechanisms, we performed a chemical suppressor screen integrating multiple human cellular and zebrafish in vivo endpoints. This screen identified Cx43 (connexin 43) hemichannel blockade as a robust suppressor of the abnormal phenotypes in both models of MYL4 (myosin light chain 4)–related atrial cardiomyopathy. Immunofluorescence and coimmunoprecipitation studies revealed an interaction between MYL4 and Cx43 with altered localization of Cx43 hemichannels to the lateral membrane in MYL4 mutants, as well as in atrial biopsies from unselected forms of human AF. The membrane fraction from MYL4-/- human embryonic stem cell derived atrial cells demonstrated increased phospho-Cx43, which was further accentuated by retinoic acid treatment and by the presence of risk alleles at the Pitx2 locus. PKC (protein kinase C) was induced by retinoic acid, and PKC inhibition also rescued the abnormal phenotypes in the atrial cardiomyopathy models.Conclusions:These data establish a mechanistic link between the transcriptional, metabolic and electrical pathways previously implicated in AF substrate and suggest novel avenues for the prevention or therapy of this common arrhythmia.</p

Expression analysis of carbohydrate antigens in ductal carcinoma in situ of the breast by lectin histochemistry

<p>Abstract</p> <p>Background</p> <p>The number of breast cancer patients diagnosed with ductal carcinoma <it>in situ </it>(DCIS) continues to grow. Laboratory and clinical data indicate that DCIS can progress to invasive disease. Carbohydrate-mediated cell-cell adhesion and tumor-stroma interaction play crucial roles in tumorigenesis and tumor aggressive behavior. Breast carcinogenesis may reflect quantitative as well as qualitative changes in oligosaccharide expression, which may provide a useful tool for early detection of breast cancer. Because tumor-associated carbohydrate antigens (TACA) are implicated in tumor invasion and metastasis, the purpose of this study was to assess the expression of selected TACA by lectin histochemistry on DCIS specimens from the archival breast cancer tissue array bank of the University of Arkansas for Medical Sciences.</p> <p>Methods</p> <p>For detection of TACA expression, specimens were stained with <it>Griffonia simplicifolia </it>lectin-I (GS-I) and <it>Vicia vilosa </it>agglutinin (VVA). We studied associations of lectin reactivity with established prognostic factors, such as tumor size, tumor nuclear grade, and expression of Her-2/neu, p53 mutant and estrogen and progesterone receptors.</p> <p>Results</p> <p>We observed that both lectins showed significant associations with nuclear grade of DCIS. DCIS specimens with nuclear grades II and III showed significantly more intense reactivity than DCIS cases with nuclear grade I to GS-1 (Mean-score chi-square = 17.60, DF = 2; <it>P </it>= 0.0002) and VVA (Mean-score chi-square = 15.72, DF = 2; <it>P </it>= 0.0004).</p> <p>Conclusion</p> <p>The results suggest that the expression of VVA- and GS-I-reactive carbohydrate antigens may contribute to forming higher grade DCIS and increase the recurrence risk.</p

Organization of Physical Interactomes as Uncovered by Network Schemas

Large-scale protein-protein interaction networks provide new opportunities for understanding cellular organization and functioning. We introduce network schemas to elucidate shared mechanisms within interactomes. Network schemas specify descriptions of proteins and the topology of interactions among them. We develop algorithms for systematically uncovering recurring, over-represented schemas in physical interaction networks. We apply our methods to the S. cerevisiae interactome, focusing on schemas consisting of proteins described via sequence motifs and molecular function annotations and interacting with one another in one of four basic network topologies. We identify hundreds of recurring and over-represented network schemas of various complexity, and demonstrate via graph-theoretic representations how more complex schemas are organized in terms of their lower-order constituents. The uncovered schemas span a wide range of cellular activities, with many signaling and transport related higher-order schemas. We establish the functional importance of the schemas by showing that they correspond to functionally cohesive sets of proteins, are enriched in the frequency with which they have instances in the H. sapiens interactome, and are useful for predicting protein function. Our findings suggest that network schemas are a powerful paradigm for organizing, interrogating, and annotating cellular networks