Defining the orientation of the tandem fusions that occurred during the evolution of Indian muntjac chromosomes by BAC mapping

The Indian muntjac (Muntiacus muntjak vaginalis) has a karyotype of 2n=6 in the female and 7 in the male, the karyotypic evolution of which through extensive tandem fusions and several centric fusions has been well-documented by recent molecular cytogenetic studies. In an attempt to define the fusion orientations of conserved chromosomal segments and the molecular mechanisms underlying the tandem fusions, we have constructed a highly redundant (more than six times of whole genome coverage) bacterial artificial chromosome (BAC) library of Indian muntjac. The BAC library contains 124,800 clones with no chromosome bias and has an average insert DNA size of 120 kb. A total of 223 clones have been mapped by fluorescent in situ hybridization onto the chromosomes of both Indian muntjac and Chinese muntjac and a high-resolution comparative map has been established. Our mapping results demonstrate that all tandem fusions that occurred during the evolution of Indian muntjac karyotype from the acrocentric 2n=70 hypothetical ancestral karyotype are centromere-telomere (head-tail) fusions.

Range-Gated Laser Stroboscopic Imaging for Night Remote Surveillance

For night remote surveillance, we present a method, the range-gated laser stroboscopic imaging(RGLSI), which uses a new kind of time delay integration mode to integrate target signals so that night remote surveillance can be realized by a low-energy illuminated laser. The time delay integration in this method has no influence on the video frame rate. Compared with the traditional range-gated laser imaging, RGLSI can reduce scintillation and target speckle effects and significantly improve the image signal-to-noise ratio analyzed. Even under low light level and low visibility conditions, the RGLSI system can effectively work. In a preliminary experiment, we have detected and recognized a railway bridge one kilometer away under a visibility of six kilometers, when the effective illuminated energy is 29.5 mu J.

Biologically Inspired Features for Scene Classification in Video Surveillance

Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective, and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper.