MicroRNAs are small noncoding RNAs that regulate the expression of protein-coding genes. To evaluate the involvement of microRNAs in prostate cancer, we determined genome-wide expression of microRNAs and mRNAs in 60 primary prostate tumors and 16 nontumor prostate tissues. The mRNA analysis revealed that key components of micro-RNA processing and several microRNA host genes, e.g., MCM7 and C9orf5, were significantly up-regulated in prostate tumors. Consistent with these findings, tumors expressed the miR-106b-25 cluster, which maps to intron 13 of MCM7, and miR-32, which maps to intron 14 of C9orf5, at significantly higher levels than nontumor prostate. The expression levels of other microRNAs, including a number of miR-106b-25 cluster homologues, were also altered in prostate tumors. Additional differences in microRNA abundance were found between organ-confined tumors and those with extraprostatic disease extension. Lastly, we found evidence that some microRNAs are androgen-regulated and that tumor microRNAs influence transcript abundance of protein-coding target genes in the cancerous prostate. In cell culture, E2F1 and p21/WAF1 were identified as targets of miR-106b, Bim of miR-32, and exportin-6 and protein tyrosine kinase 9 of miR-1. In summary, microRNA expression becomes altered with the development and progression of prostate cancer. Some of these microRNAs regulate the expression of cancer-related genes in prostate cancer cells. [Cancer Res 2008;68(15):6162-70]

Inducible nitric oxide synthase (NOS2) is involved in wound healing

In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramér-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator.

istributed sensor networks have been proposed for a wide range of applications. The main purpose of a sensor network is to monitor an area, including detecting, identifying, localizing, and tracking one or more objects of interest. These networks may be used by the military in surveillance, reconnaissance, and combat scenarios or around the perimeter of a manufacturing plant for intrusion detection. In other applications such as hearing aids and multimedia, microphone networks are capable of enhancing audio signals under noisy conditions for improved intelligibility, recognition, and cuing for camera aiming. Recent developments in integrated circuit technology have allowed the construction of low-cost miniature sensor nodes with signal processing and wireless communication capabilities. These technological advances not only open up many possibilities but also introduce challenging issues for the collaborative processing of wideband acoustic and seismic signals for source localization and beamforming in an energy-constrained distributed sensor network. The purpose of this article is to provide an overview of these issues. Some prior systems include: WINS at RSC/UCLA [1], AWAIRS at UCLA/RSC [2]-[4], Smart Dust at UC Berkeley [5], USC-ISI network [6], MIT network [7], SensIT systems/networks [8], and ARL Federated Laboratory Advanced Sensor Program systems/networks [9]. In the first section, we consider the physical features of the sources and their propagation properties and discuss the system features of the sensor network. The next section introduces some early works in source localization, DOA estimation, and beamforming. Other topics discussed include the closed-form least-squares source localization problem, iterative ML source localization, and DOA estimation. In the final section a brief conclusion is given. Microsensor Networks Physical Features We first characterize the basic physical characteristics and features of the sources and their propagation properties as shown in Table 1. These features are outside the control of the designer of the architecture and algorithm for the sensor network. In this article, we will deal with these features in terms of acoustic or seismic (i.e., vibrational) sources. While these two sources have some common features, they also have some distinct differences. Radio frequency (RF), visual, infrared, and magnetic sources have other distinct features but will not be considered here. The movement of personnel, car, truck, wheeled/tracked vehicle, as well as vibrating machinery can all generate acoustic or seismic waveforms. These waveforms are referred to as wideband signals since the ratio of highest to lowest frequency component is quite large. For audio waveforms (i.e., 30 Hz-15 KHz), the ratio is about 500, and these waveforms are wideband. Dominant acoustical waveforms generated from wheeled and tracked vehicles may range from 20 Hz-2 KHz, resulting in a ratio of about 100. Similarly, dominant seismic waveforms generated from wheeled vehicles may range from 5 Hz-500 Hz, al...