Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist which can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. Thus in this paper, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this paper is to compare performances measures achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combination of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed

Dexamethasone Administration During Definitive Radiation and Temozolomide Renders a Poor Prognosis in a Retrospective Analysis of Newly Diagnosed Glioblastoma Patients

BACKGROUND: Dexamethasone (DXM) is commonly used in the management of cerebral edema in patients diagnosed with glioblastoma multiforme (GBM). Bevacizumab (BEV) is FDA-approved for the progression or recurrence of GBM but has not been shown to improve survival when given for newly diagnosed patients concurrently with radiation (RT) and temozolomide (TMZ). Both DXM and BEV reduce cerebral edema, however, DXM has been shown to induce cytokine cascades which could interfere with cytotoxic therapy. We investigated whether DXM would reduce survival of GBM patients in the setting of concurrent TMZ and BEV administration. METHODS: We reviewed the treatment of all 73 patients with GBM who received definitive therapy at our institution from 2005 to 2013 with RT (60 Gy) delivered with concurrent daily TMZ (75 mg/m2). Of these, 34 patients also were treated with concurrent BEV (10 mg/kg every two weeks). Patients received adjuvant therapy (TMZ or TMZ/Bev) until either progression, discontinuation due to toxicity, or 12 months after radiation completion. All patients who had GBM progression with TMZ were offered BEV for salvage therapy, with 19 (56 %) receiving BEV. RESULTS: With a median follow-up of 15.6 months, 67 (91.8 %) patients were deceased. The OS for the entire cohort was 15.9 months, while the PFS was 7.7 months. The extent of resection was a prognostic indicator for OS (p  = .0044). The median survival following gross tumor resection (GTR) was 22.5 months, subtotal resection (STR) was 14.9 months, and biopsy was 12.1 months. The addition of BEV to TMZ with RT was borderline significantly associated with increased PFS (9.4 vs. 5.1 months, p = 0.0574) although was not significantly associated with OS (18.1 vs. 15.3 months respectively, p  = 0.3064). In patients receiving TMZ, DXM use concurrent with RT was a poor prognostic indicator of both OS (12.7 vs. 22.6 months, p = 0.003) and PFS (3.6 vs. 8.4 months, p p = 0.4818). On multivariable analysis, DXM use predicted an unfavorable OS hazard ratio (HR) = 1.72, p = 0.045). CONCLUSIONS: Our results with TMZ, BEV, and RT are similar to previous studies in terms of PFS and OS. DXM use during RT with concurrent TMZ correlated with reduced OS and PFS unless BEV was administered

Rational Zika vaccine design via the modulation of antigen membrane anchors in chimpanzee adenoviral vectors

Zika virus (ZIKV) emerged on a global scale and no licensed vaccine ensures long-lasting anti-ZIKV immunity. Here we report the design and comparative evaluation of four replication-deficient chimpanzee adenoviral (ChAdOx1) ZIKV vaccine candidates comprising the addition or deletion of precursor membrane (prM) and envelope, with or without its transmembrane domain (TM). A single, non-adjuvanted vaccination of ChAdOx1 ZIKV vaccines elicits suitable levels of protective responses in mice challenged with ZIKV. ChAdOx1 prME ∆TM encoding prM and envelope without TM provides 100% protection, as well as long-lasting anti-envelope immune responses and no evidence of in vitro antibody-dependent enhancement to dengue virus. Deletion of prM and addition of TM reduces protective efficacy and yields lower anti-envelope responses. Our finding that immunity against ZIKV can be enhanced by modulating antigen membrane anchoring highlights important parameters in the design of viral vectored ZIKV vaccines to support further clinical assessments

Disease-specific oligodendrocyte lineage cells arise in multiple sclerosis

Multiple sclerosis (MS) is characterized by an immune system attack targeting myelin, which is produced by oligodendrocytes (OLs). We performed single-cell transcriptomic analysis of OL lineage cells from the spinal cord of mice induced with experimental autoimmune encephalomyelitis (EAE), which mimics several aspects of MS. We found unique OLs and OL precursor cells (OPCs) in EAE and uncovered several genes specifically alternatively spliced in these cells. Surprisingly, EAE-specific OL lineage populations expressed genes involved in antigen processing and presentation via major histocompatibility complex class I and II (MHC-I and -II), and in immunoprotection, suggesting alternative functions of these cells in a disease context. Importantly, we found that disease-specific oligodendroglia are also present in human MS brains and that a substantial number of genes known to be susceptibility genes for MS, so far mainly associated with immune cells, are expressed in the OL lineage cells. Finally, we demonstrate that OPCs can phagocytose and that MHC-II-expressing OPCs can activate memory and effector CD4-positive T cells. Our results suggest that OLs and OPCs are not passive targets but instead active immunomodulators in MS. The disease-specific OL lineage cells, for which we identify several biomarkers, may represent novel direct targets for immunomodulatory therapeutic approaches in MS

A comprehensive view of dusky shark bycatch within the Atlantic Ocean and Gulf of Mexico

The dusky shark, a prohibited species, is managed under the dual authority of the Magnuson-Stevens Act and the Atlantic tuna Convention Act (Final Consolidated 2006). Stock assessments performed by the National Marine Fisheries Service indicate that since the 1990s, dusky shark populations are and continue to be over fished (Marshall et al., 2015). In order to reconcile the various estimates of dusky shark bycatch mortality within the commercial fishing sector, the 2015 SAFE Report, the first edition of U.S. National Bycatch Report, and the Southeast Fisheries Science Center\u27s Pelagic Longline, Shark Bottom Longline, and Southeast Gillnet Observer Programs were evaluated. It was determined that despite the dusky shark\u27s designation as a prohibition species in 2000, observer dat summary reports show that at least 408 of these animals were caught by pelagic longline vessels between the yeas of 2005-2011. Bottom longline observers aboard vessels participating in the Shark Research Fishery recorded 788 dusky shark interactions between the years of 2009-2014, and for the years of 2005-2012, 27 dusky sharks were observed in the southeast gillnet fishery. These numbers indicate that despite management measures focused on habitat effects, gear technology and deployment, and handling and release procedures, dusky shark populations continue to experience high fishing pressure

Modeling the reliability and robustness of critical infrastructure networks

Critical infrastructure systems form the foundation for the economic prosperity, security, and public health of the modern world. These complex, interdependent systems are prone to failures from causes such as natural hazards (e.g., hurricanes), terrorism, and deterioration of aging components, which can result in severe disruptions to critical services provided to society. Therefore, to minimize threats to society posed by failures in infrastructure systems, it is important to conduct risk and reliability analyses to identify and address system vulnerabilities. However, the large geographic scale and the high degree of complexity within and between infrastructure systems pose significant challenges for modeling the performance and reliability of infrastructure systems. Thus, this dissertation addresses deficiencies in current methods for modeling infrastructure system reliability by developing approaches that reflect physical and engineering details governing network performance, yet are also scalable to complex systems covering large geographic areas. The objectives of this work are achieved through the completion of three projects. Chapter 2 examines the relationship between network topology and network robustness to random failures and targeted attacks for randomly generated networks. I demonstrate that there is a statistically significant relationship between the initial topological properties of scale-free networks and their corresponding robustness to both random failures and targeted attacks. I also use this statistical approach to accurately estimate network robustness to failures for real-world networks. Chapter 3 compares topological and physical performance models for quantifying performance of electric power networks. I present a classification for different types of functional models that can be used for risk and vulnerability analysis of electric power systems, and compare the estimates of system performance obtained with these models to an AC power flow model. I show that in general, the greater the inclusion of physical characteristics of the system in a functional model, the better the estimate of the system’s actual performance when perturbed. Additionally, I demonstrate that statistical models combining simplified topological measures can be used as a surrogate for physical flow models for predicting electric power system performance after failures. Finally, Chapter 4 applies an approach for modeling ecological networks to modeling interdependent infrastructure systems. Here, I demonstrate the use of `Muir webs' for capturing additional dependencies within and between infrastructure systems (e.g., power supply to pumps in water systems) and management factors (e.g., availability of operators). I show that the Muir web approach provides the basis for a more realistic representation and estimation of the performance and reliability of interdependent infrastructure systems. The work presented in this dissertation represents a significant contribution to the field of infrastructure risk and reliability analysis. The relative simplicity of the models developed here, both in required data and in computational complexity, makes them a highly practical and efficient tool for aiding real-world decision-making. And, incorporating important physical and engineering details of infrastructure system behavior ensures that the guidance they provide to decision-makers allows for optimal improvements to system reliability