To solve the problem of immune incompatibility, nuclear transplantation has been envisaged as a means to produce cells or tissues for human autologous transplantation. Here we have derived embryonic stem cells by the transfer of human somatic nuclei into rabbit oocytes. The number of blastocysts that developed from the fused nuclear transfer was comparable among nuclear donors at ages of 5, 42, 52 and 60 years, and nuclear transfer (NT) embryonic stem cells (ntES cells) were subsequently derived from each of the four age groups. These results suggest that human somatic nuclei can form ntES cells independent of the age of the donor. The derived ntES cells are human based on karyotype, isogenicity, in situ hybridization, PCR and immunocytochemistry with probes that distinguish between the various species. The ntES cells maintain the capability of sustained growth in an undifferentiated state, and form embryoid bodies, which, on further induction, give rise to cell types such as neuron and muscle, as well as mixed cell populations that express markers representative of all three germ layers. Thus, ntES cells derived from human somatic cells by NT to rabbit eggs retain phenotypes similar to those of conventional human ES cells, including the ability to undergo multilineage cellular differentiation.

China is facing huge environmental problems, with its current rapid rate of urbanization and industrialization causing biodiversity loss, ecosystem degradation, and land resources degradation on a major scale. To overcome management conflicts and secure ecosystem services, China has proposed a new 'ecological redline policy' (ERP) using ecosystem services as a way to meet its targets. By giving environmental policy redline status, China is demonstrating strong commitment in its efforts to tackle environmental degradation and secure ecosystem services for the future. This is already having impact, as the Chinese Ministry of Environmental Protection and the National Development and Reform Commission are prepared to work together to implement the new environmental policy.

No abstract

Data sets with a large numbers of nominal variables, including some with large number of distinct values, are becoming increasingly common and need to be explored. Unfortunately, most existing visual exploration tools are designed to handle numeric variables only. When importing data sets with nominal values into such visualization tools, most solutions to date are rather simplistic. Often, techniques that map nominal values to numbers do not assign order or spacing among the values in a manner that conveys semantic relationships. Moreover, displays designed for nominal variables usually cannot handle high cardinality variables well. This paper addresses the problem of how to display nominal variables in general-purpose visual exploration tools designed for numeric variables. Specifically, we investigate (1) how to assign order and spacing among the nominal values, and (2) how to reduce the number of distinct values to display. We propose a new technique, called the DistanceQuantification-Classing (DQC) approach, to preprocess nominal variables before being imported into a visual exploration tool. In the Distance Step, we identify a set of independent dimensions that can be used to calculate the distance between nominal values. In the Quantification Step, we use the independent dimensions and the distance information to assign order and spacing among the nominal values. In the Classing Step, we use results from the previous steps to determine which values within the domain of a variable are similar to each other and thus can be grouped together. Each step in the DQC approach can be accomplished by a variety of techniques. We extended the XmdvTool package to incorporate this approach. We evaluated our approach on several data sets using a variety of measures. Information Visualization (2004) 3, 80-95.

Real-world data is known to be imperfect, suffering from various forms of defects such as sensor variability, estimation errors, uncertainty, human errors in data entry, and gaps in data gathering. Analysis conducted on variable quality data can lead to inaccurate or incorrect results. An effective visualization system must make users aware of the quality of their data by explicitly conveying not only the actual data content, but also its quality attributes. While some research has been conducted on visualizing uncertainty in spatiotemporal data and univariate data, little work has been reported on extending this capability into multivariate data visualization. In this paper we describe our approach to the problem of visually exploring multivariate data with variable quality. As a foundation, we propose a general approach to defining quality measures for tabular data, in which data may experience quality problems at three granularities: individual data values, complete records, and specific dimensions. We then present two approaches to visual mapping of quality information into display space. In particular, one solution embeds the quality measures as explicit values into the original dataset by regarding value quality and record quality as new data dimensions. The other solution is to superimpose the quality information within the data visualizations using additional visual variables. We also report on user studies conducted to assess alternate mappings of quality attributes to visual variables for the second method. In addition, we describe case studies that expose some of the advantages and disadvantages of these two approaches.

Micro-electro-mechanical system (MEMS) accelerometer-based inclinometers are widely used to measure deformations of civil structures. To further improve the measurement accuracy, a new calibration technique was proposed in this paper. First, a single-parameter calibration model was constructed to obtain accurate angles. Then, an image-processing-based method was designed to obtain the key parameter for the calibration model. An ADXL355 accelerometer-based inclinometer was calibrated to evaluate the feasibility of the technique. In this validation experiment, the technique was proven to be reliable and robust. Finally, to evaluate the performance of the technique, the calibrated MEMS inclinometer was used to measure the deflections of a scale beam model. The experimental results demonstrate that the proposed technique can yield accurate deformation measurements for MEMS inclinometers.

Location localization technology is used in a number of industrial and civil applications. Real time location localization accuracy is highly dependent on the quality of the distance measurements and efficiency of solving the localization equations. In this paper, we provide a novel approach to solve the nonlinear localization equations efficiently and simultaneously eliminate the bad measurement data in range-based systems. A geometric intersection model was developed to narrow the target search area, where Newton’s Method and the Direct Search Method are used to search for the unknown position. Not only does the geometric intersection model offer a small bounded search domain for Newton’s Method and the Direct Search Method, but also it can self-correct bad measurement data. The Direct Search Method is useful for the coarse localization or small target search domain, while the Newton’s Method can be used for accurate localization. For accurate localization, by utilizing the proposed Modified Newton’s Method (MNM), challenges of avoiding the local extrema, singularities, and initial value choice are addressed. The applicability and robustness of the developed method has been demonstrated by experiments with an indoor system.

No abstract