Complex Simplicity

Artwor

Reservoir System for Tissue-on-a-Chip

Dr. Christopher Heylman’s research laboratory needs an improved reservoir system for their microfluidic device that reduces flow variance, failures, and the physical footprint. Current microfluidic devices have various types of reservoir systems, including vials, 96-well plates, and other cylindrical wells. Several patents and standards must be considered when designing the reservoir system. To develop a prototype the following design process was used: product discovery, project planning, product definition, conceptual design, product development. Dr. Heylman expressed various customer requirements including increased time between cell media changes, reduced leaks and blockages, and reduced flow variance. These were then translated into engineering requirements. Target values were set for these engineering requirements based on the current microfluidic device, other reservoir systems, and other information. Timelines, deadlines, and milestones were outlined for the tasks in each step of the design process. A morphology was used to create concept sketches for our project and a Pugh Matrix was used to evaluate the best concept. A CAD model was created for our concept and fluids calculations were performed to evaluate if the concept would meet the fluidic requirements. A COMSOL model was created to simulate fluid flow through the microfluidic chip and a failure modes and effects analysis was performed. Our CAD model was modified for our final design and detailed drawings were created. Dimensioning, costs, and material selection for our design are discussed, as well as manufacturing instructions and detailed test protocols. Test criteria included 2D surface area, device chamber dimensions, reservoir diameter, volume, devices without leaks, channels with flow, sterilizability, opacity (compared to old device), cell viability, time between media changes, flow velocity, flow velocity standard deviation, and cost. After analyzing the testing results, it was determined at all engineering specifications were met except for devices without leaks and channels with flow. After an explanation of how to use the platform, interpretations from testing were discussed as well as future directions for the project

Treebank-based acquisition of a Chinese lexical-functional grammar

Scaling wide-coverage, constraint-based grammars such as Lexical-Functional Grammars (LFG) (Kaplan and Bresnan, 1982; Bresnan, 2001) or Head-Driven Phrase Structure Grammars (HPSG) (Pollard and Sag, 1994) from fragments to naturally occurring unrestricted text is knowledge-intensive, time-consuming and (often prohibitively) expensive. A number of researchers have recently presented methods to automatically acquire wide-coverage, probabilistic constraint-based grammatical resources from treebanks (Cahill et al., 2002, Cahill et al., 2003; Cahill et al., 2004; Miyao et al., 2003; Miyao et al., 2004; Hockenmaier and Steedman, 2002; Hockenmaier, 2003), addressing the knowledge acquisition bottleneck in constraint-based grammar development. Research to date has concentrated on English and German. In this paper we report on an experiment to induce wide-coverage, probabilistic LFG grammatical and lexical resources for Chinese from the Penn Chinese Treebank (CTB) (Xue et al., 2002) based on an automatic f-structure annotation algorithm. Currently 96.751% of the CTB trees receive a single, covering and connected f-structure, 0.112% do not receive an f-structure due to feature clashes, while 3.137% are associated with multiple f-structure fragments. From the f-structure-annotated CTB we extract a total of 12975 lexical entries with 20 distinct subcategorisation frame types. Of these 3436 are verbal entries with a total of 11 different frame types. We extract a number of PCFG-based LFG approximations. Currently our best automatically induced grammars achieve an f-score of 81.57% against the trees in unseen articles 301-325; 86.06% f-score (all grammatical functions) and 73.98% (preds-only) against the dependencies derived from the f-structures automatically generated for the original trees in 301-325 and 82.79% (all grammatical functions) and 67.74% (preds-only) against the dependencies derived from the manually annotated gold-standard f-structures for 50 trees randomly selected from articles 301-325