A Statistical Graphical Model of the California Reservoir System

The recent California drought has highlighted the potential vulnerability of the state's water management infrastructure to multiyear dry intervals. Due to the high complexity of the network, dynamic storage changes in California reservoirs on a state-wide scale have previously been difficult to model using either traditional statistical or physical approaches. Indeed, although there is a significant line of research on exploring models for single (or a small number of) reservoirs, these approaches are not amenable to a system-wide modeling of the California reservoir network due to the spatial and hydrological heterogeneities of the system. In this work, we develop a state-wide statistical graphical model to characterize the dependencies among a collection of 55 major California reservoirs across the state; this model is defined with respect to a graph in which the nodes index reservoirs and the edges specify the relationships or dependencies between reservoirs. We obtain and validate this model in a data-driven manner based on reservoir volumes over the period 2003–2016. A key feature of our framework is a quantification of the effects of external phenomena that influence the entire reservoir network. We further characterize the degree to which physical factors (e.g., state-wide Palmer Drought Severity Index (PDSI), average temperature, snow pack) and economic factors (e.g., consumer price index, number of agricultural workers) explain these external influences. As a consequence of this analysis, we obtain a system-wide health diagnosis of the reservoir network as a function of PDSI

In-vitro prostate cancer biomarker detection by directed conjugation of anti-PSCA antibody to super paramagnetic iron oxide nanoparticless

Background: The main property of a successful conjugation of antibodies to nanoparticles is keeping the potency of antibody for binding the antigen, and an oriented conjugation can do that. Under such ground, this study was carried out to explore the efficiency of two conjugation methods in binding iron nanoparticles to an antibody produced against PSCA (prostate stem cell antigen) using in vitro labeling of PC3 cells. Methods: In this experimental study, we conjugated dextran-superparamagnetic iron oxide nanoparticles (dexSPIONs) to anti-PSCA antibody by two different methods, including targeting carbohydrate moieties in FC domain and the free amine group of amino acid side chains. Ultimately, Iron staining was done by anti-PSCA antibody-dexSPIONs in PC3 cells to detect antibody binding to the cells. Results: A strong blue dye was induced by iron staining in conjugated dexSPIONs on the membrane of PC3 cells by the former method than the second one. Moreover, cells treated with 20 nm diameters of dexSPIONs showed higher resolution of blue color than those treated with 100 nm nanoparticles. Conclusion: This oriented conjugation method promoted the efficiency of targeting tumor antigens, and the presence of iron particles might enhance MRI image intensity in vivo by targeting PSCA-overexpressing cells in future studies. © Iran University of Medical Sciences

A Statistical Graphical Model of the California Reservoir System

The recent California drought has highlighted the potential vulnerability of the state's water management infrastructure to multiyear dry intervals. Due to the high complexity of the network, dynamic storage changes in California reservoirs on a state-wide scale have previously been difficult to model using either traditional statistical or physical approaches. Indeed, although there is a significant line of research on exploring models for single (or a small number of) reservoirs, these approaches are not amenable to a system-wide modeling of the California reservoir network due to the spatial and hydrological heterogeneities of the system. In this work, we develop a state-wide statistical graphical model to characterize the dependencies among a collection of 55 major California reservoirs across the state; this model is defined with respect to a graph in which the nodes index reservoirs and the edges specify the relationships or dependencies between reservoirs. We obtain and validate this model in a data-driven manner based on reservoir volumes over the period 2003–2016. A key feature of our framework is a quantification of the effects of external phenomena that influence the entire reservoir network. We further characterize the degree to which physical factors (e.g., state-wide Palmer Drought Severity Index (PDSI), average temperature, snow pack) and economic factors (e.g., consumer price index, number of agricultural workers) explain these external influences. As a consequence of this analysis, we obtain a system-wide health diagnosis of the reservoir network as a function of PDSI

Achieving a Fully-Flexible Virtual Network Embedding in Elastic Optical Networks

Network operators must continuously scale the capacity of their optical backbone networks to keep apace with the proliferation of bandwidth-intensive applications. Today's optical networks are designed to carry large traffic aggregates with coarse-grained resource allocation, and are not adequate for maximizing utilization of the expensive optical substrate. Elastic Optical Network (EON) is an emerging technology that facilitates flexible allocation of fiber spectrum by leveraging finer-grained channel spacing, tunable modulation formats and Forward Error Correction (FEC) overheads, and baud-rate assignment, to right size spectrum allocation to customer needs. Virtual Network Embedding (VNE) over EON has been a recent topic of interest due to its importance for 5G network slicing. However, the problem has not yet been addressed while simultaneously considering the full flexibility offered by an EON. In this paper, we present an optimization model that solves the VNE problem over EON when lightpath configurations can be chosen among a large (and practical) set of combinations of paths, modulation formats, FEC overheads and baud rates. The VNE over EON problem is solved in its splittable version, which significantly increases problem complexity, but is much more likely to return a feasible solution. Given the intractability of the optimal solution, we propose a heuristic to solve larger problem instances. Key results from extensive simulations are: (i) a fully-flexible VNE can save up to 60% spectrum resources compared to that where no flexibility is exploited, and (ii) solutions of our heuristic fall in more than 90% of the cases, within 5% of the optimal solution, while executing several orders of magnitude faster

Reduced expression of NGEP is associated with high-grade prostate cancers: A tissue microarray analysis

New gene expressed in prostate (NGEP) is a newly diagnosed prostate-specific gene that is expressed only in normal prostate and prostate cancer cells. Discovery of tissue-specific markers may promote the development of novel targets for immunotherapy of prostate cancer. In the present study, the staining pattern and clinical significance of NGEP were evaluated in a series of prostate tissues composed of 123 prostate cancer, 19 high-grade prostatic intraepithelial neoplasia and 44 samples of benign prostate tissue included in tissue microarrays using immunohistochemistry. Our study demonstrated that NGEP localized mainly in the apical and lateral membranes and was also partially distributed in the cytoplasm of epithelial cells of normal prostate tissue. All of the examined prostate tissues expressed NGEP with a variety of intensities; the level of expression was significantly more in the benign prostate tissues compared to malignant prostate samples (P value <0.001). Among prostate adenocarcinoma samples, a significant and inverse correlation was observed between the intensity of NGEP expression and increased Gleason score (P = 0.007). Taken together, we found that NGEP protein is widely expressed in low-grade to high-grade prostate adenocarcinomas as well as benign prostate tissues, and the intensity of expression is inversely proportional to the level of malignancy. NGEP could be an attractive target for immune-based therapy of prostate cancer patients as an alternative to the conventional therapies particularly in indolent patients. © 2013 Springer-Verlag Berlin Heidelberg

Reoptimizing Network Slice Embedding on EON-enabled Transport Networks

5G transport networks will support dynamic services with diverse requirements through network slicing. Elastic Optical Networks (EONs) facilitate transport network slicing by flexible spectrum allocation and tuning of transmission configurations such as modulation format and forward error correction. A major challenge in supporting dynamic services is the lack of a priori knowledge of future slice requests. In consequence, slice embedding can become sub-optimal over time, leading to spectrum fragmentation and skewed utilization. This in turn can block future slice requests, impacting operator revenue. Therefore, operators need to periodically re-optimize slice embedding for reducing fragmentation. In this paper, we address this problem of re-optimizing network slice embedding on EONs for minimizing fragmentation. The problem is solved in its splittable version, which significantly increases problem complexity, but offers more opportunities for a larger set of re-configuration actions. We employ simulated annealing for systematically exploring the large solution space. We also propose a greedy algorithm to address the practical constraint to limit the number of re-configuration steps taken to reach a defragmentated state. Our extensive simulations demonstrate that the greedy algorithm yields a solution very close to that obtained using simulated annealing while requiring orders of magnitude lesser number of re-configuration actions