Ranking of microRNA target prediction scores by Pareto front analysis

Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure. which encourages further research towards a higher-dimensional analysis of Pareto fronts. (C) 2010 Elsevier Ltd. All rights reserved.

Sperm DNA damage measured by the alkaline Comet assay as an independent predictor of male infertility and in vitro fertilization success

Objective: To evaluate sperm DNA fragmentation and semen parameters to diagnose male factor infertility and predict pregnancy after IVF.
Design: Prospective study.
Setting: Academic research laboratory.
Patient(s): Seventy-five couples undergoing IVF and 28 fertile donors.
Intervention(s): Sperm DNA fragmentation was measured by the alkaline Comet assay in semen and sperm after density gradient centrifugation (DGC). Binary logistic regression was used to analyze odds ratios (OR) and relative risks (RR) for IVF outcomes.
Main Outcome Measure(s): Semen parameters and sperm DNA fragmentation in semen and DGC sperm compared with fertilization rates, embryo quality, and pregnancy.
Result(s): Men with sperm DNA fragmentation at more than a diagnostic threshold of 25% had a high risk of infertility (OR: 117.33, 95% confidence interval [CI]: 12.72–2,731.84, RR: 8.75). Fertilization rates and embryo quality decreased as sperm DNA fragmentation increased in semen and DGC sperm. The risk of failure to achieve a pregnancy increased when sperm DNA fragmentation exceeded a prognostic threshold value of 52% for semen (OR: 76.00, CI: 8.69–1,714.44, RR: 4.75) and 42% for DGC sperm (OR: 24.18, CI: 2.89–522.34, RR: 2.16).
Conclusion(s): Sperm DNA testing by the alkaline Comet assay is useful for both diagnosis of male factor infertility and prediction of IVF outcome.

Incremental learning from stream data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.

Methods in wind power prediction & forecasting

Globally on-shore wind power has seen considerable growth in all grid systems. In the coming decade off-shore wind power is also expected to expand rapidly. Wind power is variable and intermittent over various time scales because it is weather dependent. Therefore wind power integration into traditional grids needs additional power system and electricity market planning and management for system balancing. This extra system balancing means that there is additional system costs associated with wind power assimilation. Wind power forecasting and prediction methods are used by system operators to plan unit commitment, scheduling and dispatch and by electricity traders and wind farm owners to maximize profit. Accurate wind power forecasting and prediction has numerous challenges. This paper presents a study of the existing and possible future methods used in wind power forecasting and prediction for both on-shore and off-shore wind farms.

How Porosity and Permeability Vary Spatially With Grain Size, Sorting, Cement Volume, and Mineral Dissolution In Fluvial Triassic Sandstones: The Value of Geostatistics and Local Regression

Although it is well known that sandstone porosity and permeability are controlled by a range of parameters such as grain size and sorting, amount, type, and location of diagenetic cements, extent and type of compaction, and the generation of intergranular and intragranular secondary porosity, it is less constrained how these controlling parameters link up in rock volumes (within and between beds) and how they spatially interact to determine porosity and permeability. To address these unknowns, this study examined Triassic fluvial sandstone outcrops from the UK using field logging, probe permeametry of 200 points, and sampling at 100 points on a gridded rock surface. These field observations were supplemented by laser particle-size analysis, thin-section point-count analysis of primary and diagenetic mineralogy, quantitiative XRD mineral analysis, and SEM/EDAX analysis of all 100 samples. These data were analyzed using global regression, variography, kriging, conditional simulation, and geographically weighted regression to examine the spatial relationships between porosity and permeability and their potential controls. The results of bivariate analysis (global regression) of the entire outcrop dataset indicate only a weak correlation between both permeability porosity and their diagenetic and depositional controls and provide very limited information on the role of primary textural structures such as grain size and sorting. Subdividing the dataset further by bedding unit revealed details of more local controls on porosity and permeability. An alternative geostatistical approach combined with a local modelling technique (geographically weighted regression; GWR) subsequently was used to examine the spatial variability of porosity and permeability and their controls. The use of GWR does not require prior knowledge of divisions between bedding units, but the results from GWR broadly concur with results of regression analysis by bedding unit and provide much greater clarity of how porosity and permeability and their controls vary laterally and vertically. The close relationship between depositional lithofacies in each bed, diagenesis, and permeability, porosity demonstrates that each influences the other, and in turn how understanding of reservoir properties is enhanced by integration of paleoenvironmental reconstruction, stratigraphy, mineralogy, and geostatistics.

Quantum circuits for spin and flavor degrees of freedom of quarks forming nucleons

We discuss the quantum-circuit realization of the state of a nucleon in the scope of simple simmetry groups. Explicit algorithms are presented for the preparation of the state of a neutron or a proton as resulting from the composition of their quark constituents. We estimate the computational resources required for such a simulation and design a photonic network for its implementation. Moreover, we highlight that current work on three-body interactions in lattices of interacting qubits, combined with the measurement-based paradigm for quantum information processing, may also be suitable for the implementation of these nucleonic spin states.

Modelling and Control of Shape Memory Alloy Actuators by using Preisach Model, Genetic Algorithm and Fuzzy Logic

Shapememoryalloy (SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics and so on. Nonlinearity hysteresis effects existing in SMA actuators present a problem in the motion control of these smart actuators. This paper investigates the control problem of SMA actuators in both simulation and experiment. In the simulation, the numerical Preisachmodel with geometrical interpretation is used for hysteresis modeling of SMA actuators. This model is then incorporated in a closed loop PID control strategy. The optimal values of PID parameters are determined by using geneticalgorithm to minimize the mean squared error between desired output displacement and simulated output. However, the control performance is not good compared with the simulation results when these parameters are applied to the real SMA control since the system is disturbed by unknown factors and changes in the surrounding environment of the system. A further automated readjustment of the PID parameters using fuzzylogic is proposed for compensating the limitation. To demonstrate the effectiveness of the proposed controller, real time control experiment results are presented.

Unsteady Simulations of a Film Cooling Flow from an Inclined Cylindrical Jet

Film cooling is extensively used to provide protection against the severe thermal environment in gas turbine engines. Most of the computational studies on film cooling flow have been done using steady Reynolds-averaged Navier–Stokes calculation procedures. However, the flowfield associated with a jet in a crossflow is highly unsteady and complex with different types of vortical structures. In this paper, a computational investigation about the unsteady phenomena of a jet in a crossflow is performed using detached eddy simulation. Detailed computation of a single row of 35 deg round holes on a flat plate has been obtained for a 1.0 blowing ratio and a 2.0 density ratio. First, time-step size, grid resolution, and computational domain tests for an unsteady simulation have been conducted. Comparison between the results of unsteady Reynolds-averaged Navier–Stokes calculation, detached eddy simulation, and large eddy simulation is also performed. Comparison of the time-averaged detached eddy simulation prediction with the measured film-cooling effectiveness shows that the detached eddy simulation prediction is reasonable. From present detached eddy simulations, the influential coherent vortical structures of a film cooling flow can be seen. The unsteady physics of jet in a crossflow interactions and a jet liftoff in film cooling flows have been explained.

The impact of sperm DNA damage in assisted conception and beyond: recent advances in diagnosis and treatment

Abstract Sperm DNA damage is a useful biomarker for male infertility diagnosis and prediction of assisted reproduction outcomes.
It is associated with reduced fertilization rates, embryo quality and pregnancy rates, and higher rates of spontaneous miscarriage
and childhood diseases. This review provides a synopsis of the most recent studies from each of the authors, all of whom have major
track records in the field of sperm DNA damage in the clinical setting. It explores current laboratory tests and the accumulating body
of knowledge concerning the relationship between sperm DNA damage and clinical outcomes. The paper proceeds to discuss the
strengths, weaknesses and clinical applicability of current sperm DNA tests. Next, the biological significance of DNA damage in
the male germ line is considered. Finally, as sperm DNA damage is often the result of oxidative stress in the male reproductive tract,
the potential contribution of antioxidant therapy in the clinical management of this condition is discussed. DNA damage in human spermatozoa is an important attribute of semen quality. It should be part of the clinical work up and properly controlled trials
addressing the effectiveness of antioxidant therapy should be undertaken as a matter of urgency.

Power take-off and damping of an oscillating wave surge converter

The European Union has set a target of 20% for the share of renewable energy sources in gross final energy consumption in 2020. These renewable energy targets are priority objectives for the Europe 2020 strategy for inclusive growth. In line with the European Union renewable energy policies, the Northern Ireland Executive has a target to deliver 40% renewable electricity by 2020. Currently, Northern Ireland imports 98% of the energy it uses in the form of fossil fuels. Locally produced energy and electricity is needed to ensure sustainable development. The aim of this research is to develop part of a strategy for the mechanical power take-off system for a flap type wave energy converter. Aquamarine Power Ltd’s Oyster flap was the device used for simulation and testing purposes. In this paper the state-of-the-art of wave energy converters is reviewed and a 40th scale test model was developed and built.

The feasibility of using near infrared and Raman spectroscopic techniques to detect fraudulent adulteration of chili powders with Sudan dye

Chili powder is a globally traded commodity which has been found to be adulterated with Sudan dyes from 2003 onwards. In this study, chili powders were adulterated with varying quantities of Sudan I dye (0.1-5%) and spectra were generated using near infrared reflectance spectroscopy (NIRS) and Raman
spectroscopy (on a spectrometer with a sample compartment modified as part of the study). Chemometrics were applied to the spectral data to produce quantitative and qualitative calibration models and prediction statistics. For the quantitative models coefficients of determination (R2) were found to be
0.891-0.994 depending on which spectral data (NIRS/Raman) was processed, the mathematical algorithm used and the data pre-processing applied. The corresponding values for the root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were found to be 0.208-0.851%
and 0.141-0.831% respectively, once again depending on the spectral data and the chemometric treatment applied to the data. Indications are that the NIR spectroscopy based models are superior to the models produced from Raman spectral data based on a comparison of the values of the chemometric
parameters. The limit of detection (LOD) based on analysis of 20 blank chili powders against each calibration model gave 0.25% and 0.88% for the NIR and Raman data, respectively. In addition, adopting a qualitative approach with the spectral data and applying PCA or PLS-DA, it was possible to discriminate
between adulterated chili powders from non-adulterated chili powders.

Pre-charging and DC Fault Ride-Through of Hybri MMC Based HVDC Systems

Compared to half-bridge based MMCs, full-bridge based systems have the advantage of blocking dc fault, but at the expense of increased power semiconductors and power losses. In view of the relationships among ac/dc voltages and currents in full-bridge based MMC with the negative voltage state, this paper provides a detailed analysis on the link between capacitor voltage variation and the maximum modulation index. A hybrid MMC, consisting of mixed half-bridge and full-bridge circuits to combine their respective advantages is investigated in terms of its pre-charging process and transient dc fault ride-through capability. Simulation and experiment results demonstrate the feasibility and validity of the proposed strategy for a full-bridge based MMC and the hybrid MMC.

Energy conserving schemes for the simulation of musical instrument contact dynamics

Collisions are an innate part of the function of many musical instruments. Due to the nonlinear nature of contact forces, special care has to be taken in the construction of numerical schemes for simulation and sound synthesis. Finite difference schemes and other time-stepping algorithms used for musical instrument modelling purposes are normally arrived at by discretising a Newtonian description of the system. However because impact forces are non-analytic functions of the phase space variables, algorithm stability can rarely be established this way. This paper presents a systematic approach to deriving energy conserving schemes for frictionless impact modelling. The proposed numerical formulations follow from discretising Hamilton׳s equations of motion, generally leading to an implicit system of nonlinear equations that can be solved with Newton׳s method. The approach is first outlined for point mass collisions and then extended to distributed settings, such as vibrating strings and beams colliding with rigid obstacles. Stability and other relevant properties of the proposed approach are discussed and further demonstrated with simulation examples. The methodology is exemplified through a case study on tanpura string vibration, with the results confirming the main findings of previous studies on the role of the bridge in sound generation with this type of string instrument.

The use of handheld near-infrared reflectance spectroscopy (NIRS) for the proximate analysis of poultry feed and to detect melamine adulteration of soya bean meal

The use of handheld near infrared (NIR) instrumentation, as a tool for rapid analysis, has the potential to be used widely in the animal feed sector. A comparison was made between handheld NIR and benchtop instruments in terms of proximate analysis of poultry feed using off-the-shelf calibration models and including statistical analysis. Additionally, melamine adulterated soya bean products were used to develop qualitative and quantitative calibration models from the NIRS spectral data with excellent calibration models and prediction statistics obtained. With regards to the quantitative approach, the coefficients of determination (R2) were found to be 0.94-0.99 with the corresponding values for the root mean square error of calibration and prediction were found to be 0.081-0.215 % and 0.095-0.288 % respectively. In addition, cross validation was used to further validate the models with the root mean square error of cross validation found to be 0.101-0.212 %. Furthermore, by adopting a qualitative approach with the spectral data and applying Principal Component Analysis, it was possible to discriminate between adulterated and pure samples.