Imaging dilute contrast materials in small animals using synchrotron light

The development of a non-invasive method of visualizing gene expression in larger animals could revolutionize some aspects of gene research by opening up a wider variety of animal systems to explore; some of which may be better models of human systems. Presently, most gene expression studies employ Green Fluorescent Protein (GFP) transfected into the genome of the animal system. For larger animals, an “x-ray” equivalent of GFP would be desirable due to the high penetrating power of x-rays. A model gene modification system is to use the Sodium (Na) Iodide Symporter (NIS) which will cause the accumulation of iodine in cells which express the NIS. To non-invasively observe the dilute iodine accumulated by the cancer cells transfected with NIS in the head of small animals, such as a rat, two synchrotron-based imaging methods were studied: K-Edge Subtraction (KES) imaging and Fluorescence Subtraction Imaging (FSI). KES needs wide monochromatic x-ray beams at two energies bracketing the K-edge of the contrast agent existing or injected in the tissues. The monochromatic beam in the synchrotron facility normally is prepared by a double crystal monochromator. The appearance of the azimuthal angle (tilt error) in the double crystal monochromator creates intensity variations across the imaging field. This misalignment was studied through another two synchrotron-based imaging methods, Diffraction Enhanced Imaging (DEI) and Multi-Image Radiography (MIR), which show this problem clearly in their processed images. The detailed analysis of the effect of the tilt error, how it affects the resulting images, and how to quantify such an error were presented in the thesis. A post processing method was implemented and the artifacts caused by the improper experimental settings were discussed. With the wide monochromatic beam prepared by the double crystal monochromator, a sequence of KES experiments were done and the detection limit of KES was quantified at a projected amount of 17.5mM-cm iodine in a physical model of a rat head with a radiation dose of 2.65mGy. With the raster scan of the object relative to the monochromatic pencil beam, FSI was studied to obtain higher Signal to Noise Ratio (SNR) for local area and better detection limit compared to KES. The detection limit of FSI was measured as a projected amount of 2.5mM-cm iodine in the same physical rat head with a tolerable radiation dose of 24mGy. According to the comparison of these two imaging techniques with references to imaging time and area, radiation dose, spatial resolution, and SNR, it was concluded that these two imaging techniques can be used complementarily in imaging dilute contrast material. Due to the short imaging time and large imaging area, KES is used first to provide a global view of the object, locate the area of interest, do the preliminary diagnosis, and decide whether the further FSI is necessary. Due to its high SNR for the dilute sample, FSI can be used when the area of interest is known. The combination of these two imaging techniques will be very promising and powerful. To facilitate the comparison of KES and FSI, a quality factor was developed to evaluate the performance of the imaging system. The measured detection limits in our experiments are far beyond the thyroidal iodine concentration of a rat (around 1mM). To further improve the performance of KES, a bent Laue crystal monochromator was designed to do the simultaneous iodine KES imaging which overcomes the artifacts in the iodine image caused by the temporal difference for a single set of images. The designed monochromator can provide two separated x-ray beams bracketing the K-edge of iodine at the same time with a very high spatial resolution which is only depends on the source size, a very high energy resolution which can almost compete with that of the double crystal monochromator, and an acceptable photon flux

A Contextual Bandit Approach for Value-oriented Prediction Interval Forecasting

Prediction interval (PI) is an effective tool to quantify uncertainty and usually serves as an input to downstream robust optimization. Traditional approaches focus on improving the quality of PI in the view of statistical scores and assume the improvement in quality will lead to a higher value in the power systems operation. However, such an assumption cannot always hold in practice. In this paper, we propose a value-oriented PI forecasting approach, which aims at reducing operational costs in downstream operations. For that, it is required to issue PIs with the guidance of operational costs in robust optimization, which is addressed within the contextual bandit framework here. Concretely, the agent is used to select the optimal quantile proportion, while the environment reveals the costs in operations as rewards to the agent. As such, the agent can learn the policy of quantile proportion selection for minimizing the operational cost. The numerical study regarding a two-timescale operation of a virtual power plant verifies the superiority of the proposed approach in terms of operational value. And it is especially evident in the context of extensive penetration of wind power.Comment: submitted to IEEE Transactions on Smart Gri

Review of the 22nd National Conference on the Theoretical Study of Science Popularization in China and the International Forum on Science Communication towards 2020

The 22nd National Conference on the Theoretical Study of Science Popularization in China and the International Forum on Science Communication towards 2020 was organised by the China Research Institute for Science Popularization (CRISP) in Beijing from October 17 to October 18, 2015. Nearly 200 international and national delegates from scientific research institutions, colleges and universities, local associations for science and technology from eight countries including America, Canada, Sweden, Australia, New Zealand, India, Japan and Korea participated in the Conference

Sub-wavelength Coherent Imaging of a Pure-Phase Object with Thermal Light

We report, for the first time, the observation of sub-wavelength coherent image of a pure phase object with thermal light,which represents an accurate Fourier transform. We demonstrate that ghost-imaging scheme (GI) retrieves amplitude transmittance knowledge of objects rather than the transmitted intensities as the HBT-type imaging scheme does.Comment: 5 pages, 4 figures; Any comments pls. contact: [email protected]

Elucidating the Evolutionary Relationships among Bos taurus Digestive Organs Using Unigene Expression Data

Although the nature of ruminant evolution is still disputed, current theory based on physiology and genetic analysis suggests that the abomasum is the evolutionarily oldest stomach compartment, the rumen evolved some time after the abomasum, and the omasum is the evolutionarily youngest stomach compartment. In addition, there is some evidence of relaxed selective constraint in the stomach-like organ and the foregut shortly after the foregut formation event. Along with the assumption of a mean, stochastic rate of evolution, analysis of differences in genetic profiles among digestive body organs can give clues to the relationships among these organs. The presence of large numbers of uniquely expressed entries in the abomasum and rumen indicates either a period of relaxed selective constraint or greater evolutionary age. Additionally, differences in expression profiles indicate that the abomasum, rumen, and intestine are more closely related to each other, while the reticulum and omasum are more closely related to the rumen. Functional analysis using Gene Ontology (GO) categories also supports the proposed evolutionary relationships by identifying shared functions, such as muscle activity and development, lipid transport, and urea metabolism, between all sections of the digestive tract investigated

Value-oriented Renewable Energy Forecasting for Coordinated Energy Dispatch Problems at Two Stages

Energy forecasting is deemed an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time (referred to as the 'predict, then optimize' paradigm). However, forecast models are often developed via optimizing statistical scores while overlooking the value of the forecasts in operation. In this paper, we design a value-oriented point forecasting approach for energy dispatch problems with renewable energy sources (RESs). At the training phase, this approach incorporates forecasting with day-ahead/real-time operations for power systems, thereby achieving reduced operation costs of the two stages. To this end, we formulate the forecast model parameter estimation as a bilevel program at the training phase, where the lower level solves the day-ahead and real-time energy dispatch problems, with the forecasts as parameters; the optimal solutions of the lower level are then returned to the upper level, which optimizes the model parameters given the contextual information and minimizes the expected operation cost of the two stages. Under mild assumptions, we propose a novel iterative solution strategy for this bilevel program. Under such an iterative scheme, we show that the upper level objective is locally linear regarding the forecast model output, and can act as the loss function. Numerical experiments demonstrate that, compared to commonly used point forecasting methods, the forecasts obtained by the proposed approach result in lower operation costs in the subsequent energy dispatch problems. Meanwhile, the proposed approach is more computationally efficient than traditional two-stage stochastic program.Comment: submitted to European Journal of Operational Researc

Targeted Demand Response: Formulation, LMP Implications, and Fast Algorithms

Demand response (DR) is regarded as a solution to the issue of high electricity prices in the wholesale market, as the flexibility of the demand can be harnessed to lower the demand level for price reductions. As an across-the-board DR in a system is impractical due to the enrollment budget for instance, it is necessary to select a small group of nodes for DR implementing. Current studies resort to intuitive yet naive approaches for DR targeting, as price is implicitly associated with demand, though optimality cannot be ensured. In this paper, we derive such a relationship in the security-constrained economic dispatch via the multi-parametric programming theory, based on which the DR targeting problem is rigorously formulated as a mixed-integer quadratic programming problem aiming at reducing the averaged price to a reference level by efficiently reducing targeted nodes' demand. A solution strategy is proposed to accelerate the computation. Numerical studies demonstrate compared with the benchmarking strategy, the proposed approach can reduce the price to the reference point with less efforts in demand reduction. Besides, we empirically show that the proposed approach is immune to inaccurate system parameters, and can be generalized to variants of DR targeting tasks.Comment: submitted to IEEE Transactions on Power System