Functional enhancer elements drive subclass-selective expression from mouse to primate neocortex

Viral genetic tools to target specific brain cell types in humans and non-genetic model organisms will transform basic neuroscience and targeted gene therapy. Here we used comparative epigenetics to identify thousands of human neuronal subclass-specific putative enhancers to regulate viral tools, and 34% of these were conserved in mouse. We established an AAV platform to evaluate cellular specificity of functional enhancers by multiplexed fluorescent in situ hybridization (FISH) and single cell RNA sequencing. Initial testing in mouse neocortex yields a functional enhancer discovery success rate of over 30%. We identify enhancers with specificity for excitatory and inhibitory classes and subclasses including PVALB, LAMP5, and VIP/LAMP5 cells, some of which maintain specificity in vivo or ex vivo in monkey and human neocortex. Finally, functional enhancers can be proximal or distal to cellular marker genes, conserved or divergent across species, and could yield brain-wide specificity greater than the most selective marker genes

Performance-Based Optical Proximity Correction

Ph.DDOCTOR OF PHILOSOPH

Design for Manufacturing in IC Fabrication: Mask Cost, Circuit Performance and Convergence

Ph.DDOCTOR OF PHILOSOPH

Manufacturability Aware Design.

The aim of this work is to provide solutions that optimize the tradeoffs among design, manufacturability, and cost of ownership posed by technology scaling and sub-wavelength lithography. These solutions may take the form of robust circuit designs, cost-effective resolution technologies, accurate modeling considering process variations, and design rules assessment. We first establish a framework for assessing the impact of process variation on circuit performance, product value and return on investment on alternative processes. Key features include comprehensive modeling and different handling on die-to-die and within-die variation, accurate models of correlations of variation, realistic and quantified projection to future process nodes, and performance sensitivity analysis to improved control of individual device parameter and variation sources. Then we describe a novel minimum cost of correction methodology which determines the level of correction of each layout feature such that the prescribed parametric yield is attained with minimum RET (Resolution Enhancement Technology) cost. This timing driven OPC (Optical Proximity Correction) insertion flow uses a mathematical programming based slack budgeting algorithm to determine OPC level for all polysilicon gate geometries. Designs adopting this methodology show up to 20% MEBES (Manufacturing Electron Beam Exposure System) data volume reduction and 39% OPC runtime improvement. When the systematic correction residual errors become unavoidable, we analyze their impact on a state-of-art microprocessor's speedpath skew. A platform is created for diagnosing and improving OPC quality on gates with specific functionality such as critical gates or matching transistors. Significant changes in full-chip timing analysis indicate the necessity of a post-OPC performance verification design flow. Finally, we quantify the performance, manufacturability and mask cost impact of globally applying several common restrictive design rules. Novel approaches such as locally adapting FDRs (flexible design rules) based on image parameters range, and DRC Plus (preferred design rule enforcement with 2D pattern matching) are also described.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57676/2/jiey_1.pd

Algorithmic techniques for nanometer VLSI design and manufacturing closure

As Very Large Scale Integration (VLSI) technology moves to the nanoscale regime, design and manufacturing closure becomes very difficult to achieve due to increasing chip and power density. Imperfections due to process, voltage and temperature variations aggravate the problem. Uncertainty in electrical characteristic of individual device and wire may cause significant performance deviations or even functional failures. These impose tremendous challenges to the continuation of Moore's law as well as the growth of semiconductor industry. Efforts are needed in both deterministic design stage and variation-aware design stage. This research proposes various innovative algorithms to address both stages for obtaining a design with high frequency, low power and high robustness. For deterministic optimizations, new buffer insertion and gate sizing techniques are proposed. For variation-aware optimizations, new lithography-driven and post-silicon tuning-driven design techniques are proposed. For buffer insertion, a new slew buffering formulation is presented and is proved to be NP-hard. Despite this, a highly efficient algorithm which runs > 90x faster than the best alternatives is proposed. The algorithm is also extended to handle continuous buffer locations and blockages. For gate sizing, a new algorithm is proposed to handle discrete gate library in contrast to unrealistic continuous gate library assumed by most existing algorithms. Our approach is a continuous solution guided dynamic programming approach, which integrates the high solution quality of dynamic programming with the short runtime of rounding continuous solution. For lithography-driven optimization, the problem of cell placement considering manufacturability is studied. Three algorithms are proposed to handle cell flipping and relocation. They are based on dynamic programming and graph theoretic approaches, and can provide different tradeoff between variation reduction and wire- length increase. For post-silicon tuning-driven optimization, the problem of unified adaptivity optimization on logical and clock signal tuning is studied, which enables us to significantly save resources. The new algorithm is based on a novel linear programming formulation which is solved by an advanced robust linear programming technique. The continuous solution is then discretized using binary search accelerated dynamic programming, batch based optimization, and Latin Hypercube sampling based fast simulation

Scalable Machine Learning Methods For The Analysis Of Single-Cell Transcriptomics And Multiomics Data

Transcriptomics and proteomics-based expression profiling technologies have become increasingly popular, more affordable, and more accurate in recent years. Expression profiling of expression at the single-cell resolution allows investigators to identify rare cell subtypes in human tissue which would be otherwise confounded in lower-resolution, bulk sequencing technologies. Previously, investigators studied human cell populations by profiling RNA expression in single cells using single-cell RNA sequencing (scRNA-seq) technologies. More recently, multi-modality sequencing technologies such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) have emerged, which allow investigators to profile multiple forms of biological expression (in this case RNA and protein expression) simultaneously in the same cells. Investigators can study human biology now with greater detail than ever before, but challenges remain. (1) Cell subpopulations are not always neatly separated from one another, which makes cell type classification difficult. (2) Technical batch effects also often plague scRNA-seq studies and confound real biological signals. (3) Multi-modality technologies are excellent but remain expensive to do at scale. In this work, we seek to address these various challenges and difficulties associated with scRNA-seq and CITE-seq analyses. To address challenge (1), we propose a smooth pseudotemporal modeling approach which characterizes a cell’s identity as a mixture of two discrete identities, allowing for a continuous sliding-scale cell type rather than requiring cells to separate into discrete types. To address challenge (2), we propose an augmented autoencoder which uses a self-supervised Kullback–Leibler divergence, along with a specialized branching architecture to correct for batch effects in the full gene expression feature space. Lastly, to address challenge (3), we develop a hybrid feedforward-recurrent neural network approach which supports protein prediction, imputation, embedding, uncertainty quantification, and cell type label transfer, allowing the user to use reference CITE-seq datasets to predict and study protein expression in larger single modality RNA-only data. We validate the utility of each of our approaches using real datasets with gold standard true expression and experimentally validated cell type labels. We also demonstrate real use cases for our methods, such as improving downstream pseudotime analyses using batch correction and identifying immune response biomarkers to an H1N1 vaccine