Trends in template/fragment-free protein structure prediction

Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward

Subthalamic Glutamic Acid Decarboxylase Gene Therapy: Changes in Motor Function and Cortical Metabolism

Parkinson\u27s disease (PD) is associated with increased excitatory activity within the subthalamic nucleus (STN). We sought to inhibit STN output in hemiparkinsonian macaques by transfection with adeno-associated virus (AAV) containing the gene for glutamic acid decarboxylase (GAD). In total, 13 macaques were rendered hemiparkinsonian by right intracarotid 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine injection. Seven animals were injected with AAV-GAD into the right STN, and six received an AAV gene for green fluorescent protein (GFP). Videotaped motor ratings were performed in a masked fashion on a weekly basis over a 55-week period. At 56 weeks, the animals were scanned with 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET). Histological examination was performed at the end of the study. No adverse events were observed after STN gene therapy. We found that the clinical rating scores for the two treatment groups had different patterns of change over time (group time interaction, PP

Percutaneous spinal fixation simulation with virtual reality and haptics

BACKGROUND: In this study, we evaluated the use of a part-task simulator with 3-dimensional and haptic feedback as a training tool for percutaneous spinal needle placement. OBJECTIVE: To evaluate the learning effectiveness in terms of entry point/target point accuracy of percutaneous spinal needle placement on a high-performance augmented-reality and haptic technology workstation with the ability to control the duration of computer-simulated fluoroscopic exposure, thereby simulating an actual situation. METHODS: Sixty-three fellows and residents performed needle placement on the simulator. A virtual needle was percutaneously inserted into a virtual patient's thoracic spine derived from an actual patient computed tomography data set. RESULTS: Ten of 126 needle placement attempts by 63 participants ended in failure for a failure rate of 7.93%. From all 126 needle insertions, the average error (15.69 vs 13.91), average fluoroscopy exposure (4.6 vs 3.92), and average individual performance score (32.39 vs 30.71) improved from the first to the second attempt. Performance accuracy yielded P = .04 from a 2-sample t test in which the rejected null hypothesis assumes no improvement in performance accuracy from the first to second attempt in the test session. CONCLUSION: The experiments showed evidence (P = .04) of performance accuracy improvement from the first to the second percutaneous needle placement attempt. This result, combined with previous learning retention and/or face validity results of using the simulator for open thoracic pedicle screw placement and ventriculostomy catheter placement, supports the efficacy of augmented reality and haptics simulation as a learning tool