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In the highly dynamic metabolic landscape of a neuron, mitochondrial membrane architectures can provide critical insight into the unique energy balance of the cell. Current theoretical calculations of functional outputs like adenosine triphosphate and heat often represent mitochondria as idealized geometries, and therefore, can miscalculate the metabolic fluxes. To analyze mitochondrial morphology in neurons of mouse cerebellum neuropil, 3D tracings of complete synaptic and axonal mitochondria were constructed using a database of serial transmission electron microscopy (TEM) tomography images and converted to watertight meshes with minimal distortion of the original microscopy volumes with a granularity of 1.64 nanometer isotropic voxels. The resulting in-silico representations were subsequently quantified by differential geometry methods in terms of the mean and Gaussian curvatures, surface areas, volumes, and membrane motifs, all of which can alter the metabolic output of the organelle. Finally, we identify structural motifs present across this population of mitochondria, which may contribute to future modeling studies of mitochondrial physiology and metabolism in neurons.

In the highly dynamic metabolic landscape of a neuron, mitochondrial membrane architectures can provide critical insight into the unique energy balance of the cell. Current theoretical calculations of functional outputs like adenosine triphosphate and heat often represent mitochondria as idealized geometries, and therefore, can miscalculate the metabolic fluxes. To analyze mitochondrial morphology in neurons of mouse cerebellum neuropil, 3D tracings of complete synaptic and axonal mitochondria were constructed using a database of serial transmission electron microscopy (TEM) tomography images and converted to watertight meshes with minimal distortion of the original microscopy volumes with a granularity of 1.64 nanometer isotropic voxels. The resulting in-silico representations were subsequently quantified by differential geometry methods in terms of the mean and Gaussian curvatures, surface areas, volumes, and membrane motifs, all of which can alter the metabolic output of the organelle. Finally, we identify structural motifs present across this population of mitochondria, which may contribute to future modeling studies of mitochondrial physiology and metabolism in neurons.

In the highly dynamic metabolic landscape of a neuron, mitochondrial membrane architectures can provide critical insight into the unique energy balance of the cell. Current theoretical calculations of functional outputs like adenosine triphosphate and heat often represent mitochondria as idealized geometries, and therefore, can miscalculate the metabolic fluxes. To analyze mitochondrial morphology in neurons of mouse cerebellum neuropil, 3D tracings of complete synaptic and axonal mitochondria were constructed using a database of serial transmission electron microscopy (TEM) tomography images and converted to watertight meshes with minimal distortion of the original microscopy volumes with a granularity of 1.64 nanometer isotropic voxels. The resulting in-silico representations were subsequently quantified by differential geometry methods in terms of the mean and Gaussian curvatures, surface areas, volumes, and membrane motifs, all of which can alter the metabolic output of the organelle. Finally, we identify structural motifs present across this population of mitochondria, which may contribute to future modeling studies of mitochondrial physiology and metabolism in neurons.

We describe an automated way to generate data for a practical LLVM instruction selector based on machine-generated description of the target architecture at register transfer level. The generated instruction selector can handle arbitrarily complex machine instructions with no internal control flow, and can automatically find and take advantage of arithmetic properties of an instructions, specialized pseudo-registers and special cases of immediate operands.

This paper deals with retargetable compiler generation. After an introduction to application-specific instruction set processor design and a review of code generation in compiler backends, ISAC architecture description language is introduced. Automatic approach to instruction semantics extraction from ISAC models which result is usable for backend generation is presented. This approach was successfully tested on three models of MIPS, ARM and TI MSP430 architectures. Further backend generation process that uses extracted instruction is semantics presented. This process was currently tested on the MIPS architecture and some preliminary results are shown.

This paper deals with retargetable compiler generation. After an introduction to application-specific instruction set processor design and a review of code generation in compiler backends, ISAC architecture description language is introduced. Automatic approach to instruction semantics extraction from ISAC models which result is usable for backend generation is presented. This approach was successfully tested on three models of MIPS, ARM and TI MSP430 architectures. Further backend generation process that uses extracted instruction is semantics presented. This process was currently tested on the MIPS architecture and some preliminary results are shown.

We describe an automated way to generate data for a practical LLVM instruction selector based on machine-generated description of the target architecture at register transfer level. The generated instruction selector can handle arbitrarily complex machine instructions with no internal control flow, and can automatically find and take advantage of arithmetic properties of an instructions, specialized pseudo-registers and special cases of immediate operands.