Daniel Ma - Academia.edu (original) (raw)
Papers by Daniel Ma
American Journal of Roentgenology, 2007
OBJECTIVE. The objective of this report is to describe a previously unreported technique of selec... more OBJECTIVE. The objective of this report is to describe a previously unreported technique of selective cervical nerve block, performed from January 1, 2004, to May 19, 2006, in 560 injections, that was designed to allow continual monitoring of injectate passage and verification of needle tip position. We also illustrate faulty needle placement in a cadaveric neck. CONCLUSION. Using a short connecting tube, contrast material mixed with the final injectate, and fluoroscopy when performing a selective cervical nerve block allows continual monitoring of injectate including where washout of the original testing contrast material actually flows. A true lateral view shows a more dangerous anterior needle tip placement. In addition, performing a test with anesthetic and contrast material, waiting 1.5 minutes before administering the final injectate, and using a water-soluble steroid may provide further safety with selective cervical nerve block.
Journal of Computational Biology, 2002
A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree f... more A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum likelihood (ML) analysis. Unfortunately, searching for the maximum likelihood phylogenetic tree is computationally prohibitive for large data sets. In this paper, we describe a new algorithm that uses Structural EM for learning maximum likelihood phylogenetic trees. This algorithm is similar to the standard EM method for edge-length estimation, except that during iterations of the Structural EM algorithm the topology is improved as well as the edge length. Our algorithm performs iterations of two steps. In the E-Step, we use the current tree topology and edge lengths to compute expected sufficient statistics, which summarize the data. In the M-Step, we search for a topology that maximizes the likelihood with respect to these expected sufficient statistics. We show that searching for better topologies inside the M-step can be done efficiently, as opposed to standard methods for topology search. We prove that each iteration of this procedure increases the likelihood of the topology, and thus the procedure must converge. This convergence point, however, can be a sub-optimal one. To escape from such "local optima", we further enhance our basic EM procedure by incorporating moves in the flavor of simulated annealing. We evaluate these new algorithms on both synthetic and real sequence data, and show that for protein sequences even our basic algorithm finds more plausible trees than existing methods for searching maximum likelihood phylogenies. Furthermore, our algorithms are dramatically faster than such methods, enabling, for the first time, phylogenetic analysis of large protein data sets in the maximum likelihood framework.
American Journal of Roentgenology, 2007
OBJECTIVE. The objective of this report is to describe a previously unreported technique of selec... more OBJECTIVE. The objective of this report is to describe a previously unreported technique of selective cervical nerve block, performed from January 1, 2004, to May 19, 2006, in 560 injections, that was designed to allow continual monitoring of injectate passage and verification of needle tip position. We also illustrate faulty needle placement in a cadaveric neck. CONCLUSION. Using a short connecting tube, contrast material mixed with the final injectate, and fluoroscopy when performing a selective cervical nerve block allows continual monitoring of injectate including where washout of the original testing contrast material actually flows. A true lateral view shows a more dangerous anterior needle tip placement. In addition, performing a test with anesthetic and contrast material, waiting 1.5 minutes before administering the final injectate, and using a water-soluble steroid may provide further safety with selective cervical nerve block.
Journal of Computational Biology, 2002
A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree f... more A central task in the study of molecular evolution is the reconstruction of a phylogenetic tree from sequences of current-day taxa. The most established approach to tree reconstruction is maximum likelihood (ML) analysis. Unfortunately, searching for the maximum likelihood phylogenetic tree is computationally prohibitive for large data sets. In this paper, we describe a new algorithm that uses Structural EM for learning maximum likelihood phylogenetic trees. This algorithm is similar to the standard EM method for edge-length estimation, except that during iterations of the Structural EM algorithm the topology is improved as well as the edge length. Our algorithm performs iterations of two steps. In the E-Step, we use the current tree topology and edge lengths to compute expected sufficient statistics, which summarize the data. In the M-Step, we search for a topology that maximizes the likelihood with respect to these expected sufficient statistics. We show that searching for better topologies inside the M-step can be done efficiently, as opposed to standard methods for topology search. We prove that each iteration of this procedure increases the likelihood of the topology, and thus the procedure must converge. This convergence point, however, can be a sub-optimal one. To escape from such "local optima", we further enhance our basic EM procedure by incorporating moves in the flavor of simulated annealing. We evaluate these new algorithms on both synthetic and real sequence data, and show that for protein sequences even our basic algorithm finds more plausible trees than existing methods for searching maximum likelihood phylogenies. Furthermore, our algorithms are dramatically faster than such methods, enabling, for the first time, phylogenetic analysis of large protein data sets in the maximum likelihood framework.