The prompt optical emission that arrives with the gamma-rays from a cosmic gamma-ray burst (GRB) is a signature of the engine powering the burst, the properties of the ultra-relativistic ejecta of the explosion, and the ejecta's interactions with the surroundings. Until now, only GRB 990123 had been detected at optical wavelengths during the burst phase. Its prompt optical emission was variable and uncorrelated with the prompt gamma-ray emission, suggesting that the optical emission was generated by a reverse shock arising from the ejecta's collision with surrounding material. Here we report prompt optical emission from GRB 041219a. It is variable and correlated with the prompt gamma-rays, indicating a common origin for the optical light and the gamma-rays. Within the context of the standard fireball model of GRBs, we attribute this new optical component to internal shocks driven into the burst ejecta by variations of the inner engine. The correlated optical emission is a direct probe of the jet isolated from the medium. The timing of the uncorrelated optical emission is strongly dependent on the nature of the medium.

The observation of a prompt optical flash from GRB990123 convincingly demonstrated the value of autonomous robotic telescope systems. Pursuing a program of rapid followup observations of gamma-ray bursts, the Robotic Optical Transient Search Experiment (ROTSE) has developed a next-generation instrument, ROTSE-III, that will continue the search for fast optical transients. The entire system was designed as an economical robotic facility to be installed at remote sites throughout the world. There are seven major system components: optics, optical tube assembly, CCD camera, telescope mount,

Background: Some promising treatments for Huntington's disease (HD) may require pre-clinical testing in large animals. Minipig is a suitable species because of its large gyrencephalic brain and long lifespan. Objective: To generate HD transgenic (TgHD) minipigs encoding huntingtin (HTT)1-548 under the control of human HTT promoter. Methods: Transgenesis was achieved by lentiviral infection of porcine embryos. PCR assessment of gene transfer, observations of behavior, and postmortem biochemical and immunohistochemical studies were conducted. Results: One copy of the human HTT transgene encoding 124 glutamines integrated into chromosome 1 q24-q25 and successful germ line transmission occurred through successive generations (F0, F1, F2 and F3 generations). No developmental or gross motor deficits were noted up to 40 months of age. Mutant HTT mRNA and protein fragment were detected in brain and peripheral tissues. No aggregate formation in brain up to 16 months was seen by AGERA and filter retardation or by immunostaining. DARPP32 labeling in WT and TgHD minipig neostriatum was patchy. Analysis of 16 month old siblings showed reduced intensity of DARPP32 immunoreactivity in neostriatal TgHD neurons compared to those of WT. Compared to WT, TgHD boars by one year had reduced fertility and fewer spermatozoa per ejaculate. In vitro analysis revealed a significant decline in the number of WT minipig oocytes penetrated by TgHD spermatozoa. Conclusions:The findings demonstrate successful establishment of a transgenic model of HD in minipig that should be valuable for testing long term safety of HD therapeutics. The emergence of HD-like phenotypes in the TgHD minipigs will require more study.

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Clinical islet transplantation programs rely on the capacities of individual centers to quantify isolated islets. Current computer-assisted methods require input from human operators. Here we describe two machine learning algorithms for islet quantification: the trainable islet algorithm (TIA) and the nontrainable purity algorithm (NPA). These algorithms automatically segment pancreatic islets and exocrine tissue on microscopic images in order to count individual islets and calculate islet volume and purity. References for islet counts and volumes were generated by the fully manual segmentation (FMS) method, which was validated against the internal DNA standard. References for islet purity were generated via the expert visual assessment (EVA) method, which was validated against the FMS method. The TIA is intended to automatically evaluate micrographs of isolated islets from future donors after being trained on micrographs from a limited number of past donors. Its training ability was first evaluated on 46 images from four donors. The pixel-to-pixel comparison, binary statistics, and islet DNA concentration indicated that the TIA was successfully trained, regardless of the color differences of the original images. Next, the TIA trained on the four donors was validated on an additional 36 images from nine independent donors. The TIA was fast (67 s/image), correlated very well with the FMS method (R2=1.00 and 0.92 for islet volume and islet count, respectively), and had small REs (0.06 and 0.07 for islet volume and islet count, respectively). Validation of the NPA against the EVA method using 70 images from 12 donors revealed that the NPA had a reasonable speed (69 s/image), had an acceptable RE (0.14), and correlated well with the EVA method (R2=0.88). Our results demonstrate that a fully automated analysis of clinical-grade micrographs of isolated pancreatic islets is feasible. The algorithms described herein will be freely available as a Fiji platform plugin.