Variability of the IFN-γ ELISpot assay in the context of proficiency testing and bridging studies.

Assays that assess cellular mediated immune responses performed under Good Clinical Laboratory Practice (GCLP) guidelines are required to provide specific and reproducible results. Defined validation procedures are required to establish the Standard Operating Procedure (SOP), include pass and fail criteria, as well as implement positivity criteria. However, little to no guidance is provided on how to perform longitudinal assessment of the key reagents utilized in the assay. Through the External Quality Assurance Program Oversight Laboratory (EQAPOL), an Interferon-gamma (IFN-γ) Enzyme-linked immunosorbent spot (ELISpot) assay proficiency testing program is administered. A limit of acceptable within site variability was estimated after six rounds of proficiency testing (PT). Previously, a PT send-out specific within site variability limit was calculated based on the dispersion (variance/mean) of the nine replicate wells of data. Now an overall 'dispersion limit' for the ELISpot PT program within site variability has been calculated as a dispersion of 3.3. The utility of this metric was assessed using a control sample to calculate the within (precision) and between (accuracy) experiment variability to determine if the dispersion limit could be applied to bridging studies (studies that assess lot-to-lot variations of key reagents) for comparing the accuracy of results with new lots to results with old lots. Finally, simulations were conducted to explore how this dispersion limit could provide guidance in the number of replicate wells needed for within and between experiment variability and the appropriate donor reactivity (number of antigen-specific cells) to be used for the evaluation of new reagents. Our bridging study simulations indicate using a minimum of six replicate wells of a control donor sample with reactivity of at least 150 spot forming cells per well is optimal. To determine significant lot-to-lot variations use the 3.3 dispersion limit for between and within experiment variability.

Testing of Interposer-Based 2.5D Integrated Circuits

The unprecedented and relentless growth in the electronics industry is feeding the demand for integrated circuits (ICs) with increasing functionality and performance at minimum cost and power consumption. As predicted by Moore's law, ICs are being aggressively scaled to meet this demand. While the continuous scaling of process technology is reducing gate delays, the performance of ICs is being increasingly dominated by interconnect delays. In an effort to improve submicrometer interconnect performance, to increase packing density, and to reduce chip area and power consumption, the semiconductor industry is focusing on three-dimensional (3D) integration. However, volume production and commercial exploitation of 3D integration are not feasible yet due to significant technical hurdles.

At the present time, interposer-based 2.5D integration is emerging as a precursor to stacked 3D integration. All the dies and the interposer in a 2.5D IC must be adequately tested for product qualification. However, since the structure of 2.5D ICs is different from the traditional 2D ICs, new challenges have emerged: (1) pre-bond interposer testing, (2) lack of test access, (3) limited ability for at-speed testing, (4) high density I/O ports and interconnects, (5) reduced number of test pins, and (6) high power consumption. This research targets the above challenges and effective solutions have been developed to test both dies and the interposer.

The dissertation first introduces the basic concepts of 3D ICs and 2.5D ICs. Prior work on testing of 2.5D ICs is studied. An efficient method is presented to locate defects in a passive interposer before stacking. The proposed test architecture uses e-fuses that can be programmed to connect or disconnect functional paths inside the interposer. The concept of a die footprint is utilized for interconnect testing, and the overall assembly and test flow is described. Moreover, the concept of weighted critical area is defined and utilized to reduce test time. In order to fully determine the location of each e-fuse and the order of functional interconnects in a test path, we also present a test-path design algorithm. The proposed algorithm can generate all test paths for interconnect testing.

In order to test for opens, shorts, and interconnect delay defects in the interposer, a test architecture is proposed that is fully compatible with the IEEE 1149.1 standard and relies on an enhancement of the standard test access port (TAP) controller. To reduce test cost, a test-path design and scheduling technique is also presented that minimizes a composite cost function based on test time and the design-for-test (DfT) overhead in terms of additional through silicon vias (TSVs) and micro-bumps needed for test access. The locations of the dies on the interposer are taken into consideration in order to determine the order of dies in a test path.

To address the scenario of high density of I/O ports and interconnects, an efficient built-in self-test (BIST) technique is presented that targets the dies and the interposer interconnects. The proposed BIST architecture can be enabled by the standard TAP controller in the IEEE 1149.1 standard. The area overhead introduced by this BIST architecture is negligible; it includes two simple BIST controllers, a linear-feedback-shift-register (LFSR), a multiple-input-signature-register (MISR), and some extensions to the boundary-scan cells in the dies on the interposer. With these extensions, all boundary-scan cells can be used for self-configuration and self-diagnosis during interconnect testing. To reduce the overall test cost, a test scheduling and optimization technique under power constraints is described.

In order to accomplish testing with a small number test pins, the dissertation presents two efficient ExTest scheduling strategies that implements interconnect testing between tiles inside an system on chip (SoC) die on the interposer while satisfying the practical constraint that the number of required test pins cannot exceed the number of available pins at the chip level. The tiles in the SoC are divided into groups based on the manner in which they are interconnected. In order to minimize the test time, two optimization solutions are introduced. The first solution minimizes the number of input test pins, and the second solution minimizes the number output test pins. In addition, two subgroup configuration methods are further proposed to generate subgroups inside each test group.

Finally, the dissertation presents a programmable method for shift-clock stagger assignment to reduce power supply noise during SoC die testing in 2.5D ICs. An SoC die in the 2.5D IC is typically composed of several blocks and two neighboring blocks that share the same power rails should not be toggled at the same time during shift. Therefore, the proposed programmable method does not assign the same stagger value to neighboring blocks. The positions of all blocks are first analyzed and the shared boundary length between blocks is then calculated. Based on the position relationships between the blocks, a mathematical model is presented to derive optimal result for small-to-medium sized problems. For larger designs, a heuristic algorithm is proposed and evaluated.

In summary, the dissertation targets important design and optimization problems related to testing of interposer-based 2.5D ICs. The proposed research has led to theoretical insights, experiment results, and a set of test and design-for-test methods to make testing effective and feasible from a cost perspective.

Microbial Community Responses to Environmental Perturbation

Microorganisms mediate many biogeochemical processes critical to the functioning of ecosystems, which places them as an intermediate between environmental change and the resulting ecosystem response. Yet, we have an incomplete understanding of these relationships, how to predict them, and when they are influential. Understanding these dynamics will inform ecological principles developed for macroorganisms and aid expectations for microbial responses to new gradients. To address this research goal, I used two studies of environmental gradients and a literature synthesis.

With the gradient studies, I assessed microbial community composition in stream biofilms across a gradient of alkaline mine drainage. I used multivariate approaches to examine changes in the non-eukaryote microbial community composition of taxa (chapter 2) and functional genes (chapter 3). I found that stream biofilms at sites receiving alkaline mine drainage had distinct community composition and also differed in the composition of functional gene groups compared with unmined reference sites. Compositional shifts were not dominated by groups that could benefit from mining associated increases of terminal electron acceptors; two-thirds of responsive taxa and functional gene groups were negatively associated with mining. The majority of subsidies and stressors (nitrate, sulfate, conductivity) had no consistent relationships with taxa or gene abundances. However, methane metabolism genes were less abundant at mined sites and there was a strong, positive correlation between selenate reductase gene abundance and mining-associated selenium. These results highlighted the potential for indirect factors to also play an important role in explaining compositional shifts.

In the fourth chapter, I synthesized studies that use environmental perturbations to explore microbial community structure and microbial process connections. I examined nine journals (2009–13) and found that many qualifying papers (112 of 148) documented structure and process responses, but few (38 of 112 papers) reported statistically testing for a link. Of these tested links, 75% were significant. No particular approach for characterizing structure or processes was more likely to produce significant links. Process responses were detected earlier on average than responses in structure. Together, the findings suggested that few publications report statistically testing structure-process links; but when tested, links often occurred yet shared few commonalities in linked processes or structures and the techniques used for measuring them.

Although the research community has made progress, much work remains to ensure that the vast and growing wealth of microbial informatics data is translated into useful ecological information. In part, this challenge can be approached through using hypotheses to guide analyses, but also by being open to opportunities for hypothesis generation. The results from my dissertation work advise that it is important to carefully interpret shifts in community composition in relation to abiotic characteristics and recommend considering ecological, thermodynamic, and kinetic principles to understand the properties governing community responses to environmental perturbation.

Development and Testing of An Automatic Lung IMRT Planning Algorithm

Knowledge-based radiation treatment is an emerging concept in radiotherapy. It

mainly refers to the technique that can guide or automate treatment planning in

clinic by learning from prior knowledge. Dierent models are developed to realize

it, one of which is proposed by Yuan et al. at Duke for lung IMRT planning. This

model can automatically determine both beam conguration and optimization ob-

jectives with non-coplanar beams based on patient-specic anatomical information.

Although plans automatically generated by this model demonstrate equivalent or

better dosimetric quality compared to clinical approved plans, its validity and gener-

ality are limited due to the empirical assignment to a coecient called angle spread

constraint dened in the beam eciency index used for beam ranking. To eliminate

these limitations, a systematic study on this coecient is needed to acquire evidences

for its optimal value.

To achieve this purpose, eleven lung cancer patients with complex tumor shape

with non-coplanar beams adopted in clinical approved plans were retrospectively

studied in the frame of the automatic lung IMRT treatment algorithm. The primary

and boost plans used in three patients were treated as dierent cases due to the

dierent target size and shape. A total of 14 lung cases, thus, were re-planned using

the knowledge-based automatic lung IMRT planning algorithm by varying angle

spread constraint from 0 to 1 with increment of 0.2. A modied beam angle eciency

index used for navigate the beam selection was adopted. Great eorts were made to assure the quality of plans associated to every angle spread constraint as good

as possible. Important dosimetric parameters for PTV and OARs, quantitatively

re

ecting the plan quality, were extracted from the DVHs and analyzed as a function

of angle spread constraint for each case. Comparisons of these parameters between

clinical plans and model-based plans were evaluated by two-sampled Students t-tests,

and regression analysis on a composite index built on the percentage errors between

dosimetric parameters in the model-based plans and those in the clinical plans as a

function of angle spread constraint was performed.

Results show that model-based plans generally have equivalent or better quality

than clinical approved plans, qualitatively and quantitatively. All dosimetric param-

eters except those for lungs in the automatically generated plans are statistically

better or comparable to those in the clinical plans. On average, more than 15% re-

duction on conformity index and homogeneity index for PTV and V40, V60 for heart

while an 8% and 3% increase on V5, V20 for lungs, respectively, are observed. The

intra-plan comparison among model-based plans demonstrates that plan quality does

not change much with angle spread constraint larger than 0.4. Further examination

on the variation curve of the composite index as a function of angle spread constraint

shows that 0.6 is the optimal value that can result in statistically the best achievable

plans.

Impact of Genetic Testing and Family Health History Based Risk Counseling on Behavior Change and Cognitive Precursors for Type 2 Diabetes.

Family health history (FHH) in the context of risk assessment has been shown to positively impact risk perception and behavior change. The added value of genetic risk testing is less certain. The aim of this study was to determine the impact of Type 2 Diabetes (T2D) FHH and genetic risk counseling on behavior and its cognitive precursors. Subjects were non-diabetic patients randomized to counseling that included FHH +/- T2D genetic testing. Measurements included weight, BMI, fasting glucose at baseline and 12 months and behavioral and cognitive precursor (T2D risk perception and control over disease development) surveys at baseline, 3, and 12 months. 391 subjects enrolled of which 312 completed the study. Behavioral and clinical outcomes did not differ across FHH or genetic risk but cognitive precursors did. Higher FHH risk was associated with a stronger perceived T2D risk (pKendall < 0.001) and with a perception of "serious" risk (pKendall < 0.001). Genetic risk did not influence risk perception, but was correlated with an increase in perception of "serious" risk for moderate (pKendall = 0.04) and average FHH risk subjects (pKendall = 0.01), though not for the high FHH risk group. Perceived control over T2D risk was high and not affected by FHH or genetic risk. FHH appears to have a strong impact on cognitive precursors of behavior change, suggesting it could be leveraged to enhance risk counseling, particularly when lifestyle change is desirable. Genetic risk was able to alter perceptions about the seriousness of T2D risk in those with moderate and average FHH risk, suggesting that FHH could be used to selectively identify individuals who may benefit from genetic risk testing.