Impact of Alcohol and Drug Use on the Development of Neural Connections During Adolescence and Young Adulthood
The primary aims of this ongoing longitudinal study are to conduct a comprehensive investigation of brain development during adolescence and early adulthood and to determine how brain development is altered when individuals begin to use alcohol (as well as other drugs, such as marijuana) during this period. The researchers employ an extensive two-day data collection protocol at each study time point, consisting of behavioral assessments (interviews, questionnaires, computerized testing), brain magnetic resonance imaging (MRI; high resolution anatomical scans, several types of diffusion scans, spectroscopy, resting functional scans), and electroencephalography (EEG). They also have a one-time collection of genetic data (single-nucleotide polymorphisms). Data collection waves occur at two-year intervals and currently the researchers are completing their fifth assessment. In their analyses within and across these types of data, the researchers investigate the refinement of brain network connectivity during normal adolescent development and identify alterations due to alcohol and drug use. MSI resources are heavily used to achieve this “connectivity” aspect of this brain-behavior research, which relates directly to the goals of the Human Connectome Project. For example, using high-resolution anatomical MRI scans, the researchers extract complete representations of the cortical surface in both brain hemispheres; using diffusion MRI scans they compute measures of the microstructural organization of neural fibers that connect brain regions, and then conduct a “virtual dissection” of these fibers using probabilistic tractography; using resting functional MRI scans they measure neurophysiological activity across multiple overlapping brain networks; using EEG recordings they identify the coordinated synchronization of electrophysiological activity within brain networks in response to external stimuli; and so on. MSI resources are used in all of these analyses, for both data preprocessing and permutation-based statistics.
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Computer-Aided Design of VLSI Circuits
These researchers use MSI supercomputing resources to solve problems in the domain of computer-aided analysis and optimization of VLSI designs. Specifically, the projects will address two problems described below.
- Impact of thermal-mechanical effects on circuit performance in sub-micron planar and 3D-IC technologies: In 3D-IC technology, dies are stacked vertically and thick cylindrical shaped copper pillars (through silicon vias, TSVs) are used to connect the circuits on different layers. During manufacturing, both silicon and copper TSVs undergo annealing process with a temperature ramp from 250 degrees down to room temperature. However, due to the coefficient of thermal expansion (CTE) mismatch between copper and silicon, thermal residual stress develops inside silicon that impacts the transistor electrical properties and thereby system timing performance. This project is looking into methods for modeling these effects and capturing their impact on circuit performance. In addition, the researchers are also developing modeling and optimization methods for new transistor structures called FinFETs that use a fully three-dimensional structure along with intentional stressors inserted to enhance performance.
- Electromigration-aware power delivery network analysis: Electromigration has become a serious reliability issue while designing VLSI circuits in current technologies. There is a growing need to accurately model the effects of electromigration and then use this model within the design flow of VLSI circuits. Current models for electromigration are simple and inaccurate, and these researchers aim to come up with electromigration model for the interconnects (metal wires) in the power delivery network that is computationally efficient while physically accurate, and further apply the model to analyze the power delivery network, which typically has hundreds of thousands of metal wires for a single chip. Electromigration is also intimately linked with on-chip residual stress due to CTE mismatches between various constituents of the chip, as well as the stress build-up due to the gradient of atomic concentration. The goal of this project is to analyze the impact of these effects.
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Genomic Analysis of Inheritance in Maize
The Springer lab uses genomic technologies such as high-throughput sequencing to study the molecular sources of phenotypic variation. Their research aims to understand how variation in gene expression levels or epigenetic modifications contributes to phenotypic differences in maize. The current focus of their research is performing DNA methylation profiling and expression profiling for a set of over 100 diverse maize lines in order to associate epigenetic changes with altered gene expression levels or phenotypes. MSI software and computer labs have been used to perform data analyses and visualization of complex datasets.
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Clock distribution networks are a significant source of power consumption and a major design bottleneck for digital circuits, particularly with increasing variability. Completely asynchronous design methodologies have been studied for decades, but these have never gained widespread acceptance. These researchers have proposed an alternative: splitting digital circuitry into small blocks and synchronizing these locally with independent, cheap clocks (generated with simple inverter rings). This is feasible if one adopts a stochastic representation for signal values. Logical computation is performed on randomized bit streams, with signal values encoded in the statistics of the streams. This project is exploring extensions and applications of these ideas to molecular computing. DNA-based computation via strand displacement is the target experimental chassis.
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