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ElHefnawi M.,National Research Center of Egypt | ElHefnawi M.,Egypt Japan University of Science and Technology | ElHefnawi M.,Sudan University of Science and Technology | Hassan N.,Egypt Japan University of Science and Technology | And 7 more authors.
Bioinformatics | Year: 2011

Motivation: There is an urgent need for new medications to combat influenza pandemics. Methods: Using the genome analysis of the influenza A virus performed previously, we designed and performed a combinatorial exhaustive systematic methodology for optimal design of universal therapeutic small interfering RNA molecules (siRNAs) targeting all diverse influenza A viral strains. The rationale was to integrate the factors for highly efficient design in a pipeline of analysis performed on possible influenza-targeting siRNAs. This analysis selects specific siRNAs that has the ability to target highly conserved, accessible and biologically significant regions. This would require minimal dosage and side effects. Results and Discussion: First, >6000 possible siRNAs were designed. Successive filtration followed where a novel method for siRNA scoring filtration layers was implemented. This method excluded siRNAs below the 90% experimental inhibition mapped scores using the intersection of 12 different scoring algorithms. Further filtration of siRNAs is done by eliminating those with offtargets in the human genome and those with undesirable properties and selecting siRNA targeting highly probable single-stranded regions. Finally, the optimal properties of the siRNA were ensured through selection of those targeting 100% conserved, biologically functional short motifs. Validation of a predicted active (sh114) and a predicted inactive (sh113) (that was filtered out in Stage 8) silencer of the NS1 gene showed significant inhibition of the NS1 gene for sh114, with negligible decrease for sh113 which failed target accessibility. This demonstrated the fertility of this methodology. © The Author 2011. Published by Oxford University Press.


Wang F.,French National Center for Scientific Research | Wang F.,Dalian University of Technology | Saavedra Valeriano O.C.,Tokyo Institute of Technology | Saavedra Valeriano O.C.,Egypt Japan University of Science and Technology | Sun X.,Dalian University of Technology
Water Resources Management | Year: 2013

This study aims to develop a multi-objective optimization model in a multi-reservoir system during flood season using Numerical Weather Predictions (NWPs) outputs (short forecast). The optimization model was coupled with the Water and Energy Budget-based Distributed Hydrological Model that was used to forecast the reservoir inflows. The model was forced by 8-day lead time global deterministic NWPs by Japan Meteorological Agency. The reservoir objective function was established by considering the reservoir and upstream safety, downstream safety and future water use. The model was applied to the Baishan-Fengman multi-reservoir system of Northeast China. The results have demonstrated the model with high efficiency in optimizing reservoir objectives for all of the reservoirs. The sensitivity of the system to lead time and decision time were investigated. With the decreasing of lead time, the dam release peaks decrease and the end water levels increase. This is mainly due to the fact that the model with longer lead time needs to keep storage capacity for detected floods during long lead time period. The variation amplitude of dam releases and water levels decrease with the increasing of decision time due to the smoothing of floods and dam releases during long decision period. The model is easy to operate and is able to be coupled with other hydrological models or earth system models. © 2013 Springer Science+Business Media Dordrecht.


Wang F.,Dalian University of Technology | Wang F.,CAS Institute of Tibetan Plateau Research | Wang F.,University of Tokyo | Wang L.,CAS Institute of Tibetan Plateau Research | And 6 more authors.
Water Resources Research | Year: 2012

Future streamflow uncertainties hinder reservoir real-time operation, but the ensemble prediction technique is effective for reducing the uncertainties. This study aims to combine ensemble hydrological predictions with real-time multiobjective reservoir optimization during flood season. The ensemble prediction-based reservoir optimization system (EPROS) takes advantage of 8 day lead time global numerical weather predictions (NWPs) by the Japan Meteorological Agency (JMA). Thirty-member ensemble streamflows are generated through running the water and energy budget-based distributed hydrological model fed with 30-member perturbed quantitative precipitation forecasts (QPFs) and deterministic NWPs. The QPF perturbation amplitudes are calculated from the QPF intensity and location errors during previous 8 day periods. The reservoir objective function is established to minimize the maximum reservoir water level (reservoir and upstream safety), the downstream flood peak (downstream safety), and the difference between simulated reservoir end water level and target level (water use). The system is evaluated on the Fengman reservoir basin (semiarid), which often suffers from extreme floods in summer and serious droughts in spring. The results show the ensemble QPFs generated by EPROS are comparable to those for JMA by using probability-based measures. The streamflow forecast error is significantly reduced by employing the ensemble prediction approach. The system has demonstrated high efficiency in optimizing reservoir objectives for both normal and critical flood events. Fifty-member ensembles generate a wider streamflow and reservoir release range than 10-member ensembles, but the ensemble mean end water levels and releases are comparable. The system is easy to operate and thereby feasible for practical operations in various reservoir basins. © 2012. American Geophysical Union. All Rights Reserved.


Valeriano O.C.S.,University of Tokyo | Valeriano O.C.S.,Tokyo Institute of Technology | Valeriano O.C.S.,Egypt Japan University of Science and Technology | Koike T.,University of Tokyo | And 5 more authors.
Water Resources Research | Year: 2010

This study proposes a decision support system for real-time dam operation during heavy rainfall. It uses an operational mesoscale quantitative precipitation forecast (QPF) to force a hydrological model and considers the forecast error from the previous time step, which is introduced as a perturbation range applied to the most recent QPF. A weighting module accounts for the location, intensity, and extent of the error. Missing precipitation intensities within contributing areas and information from surrounding areas can both be considered. Forecast error is defined as the ratio of QPF to the observed precipitation within an evaluation zone (sub-basin, basin, buffer, or total domain). An objective function is established to minimize the flood volume at control points downstream and to maximize reservoir storage. The decision variables are the dam releases, which are constrained to the ensemble streamflow's information. A prototype was applied to one of the most important river basins in Japan, the Tone reservoir system. The efficiency of the approach was evident in reduced flood peaks downstream and increased water storage. The results from three events indicate that the developed decision support system is feasible for real-life dam operation. © 2010 by the American Geophysical Union.

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