Yellow Perch Population Assessment in Southwestern Lake Michigan, Including Evaluation of Sampling Techniques and the Identification Factors that Determine Yellow Perch Year-Class Strength April 1, 1996 - March 31, 1997

F-123-R; issued June 1, 1997; two different reports were issued from the Center for Aquatic Ecology with report number 1997 (9)

Yellow Perch population assessment in Southwestern Lake Michigan, including evaluation of sampling techniques and identification of the factors that determine Yellow Perch year-class strength April 1, 2004 - March 31, 2005

ID: 8528; Annual Report to Division of Fisheries, Illinois Department of Natural Resources; F-123-R-11

Yellow Perch Population Assessment in Southwestern Lake Michigan, Including Evaluation of Sampling Techniques and Identification of factors that Determine Yellow Perch Year-Class Strength April 1, 2005 - March 31, 2006

Annual Report, Federal Aid Project F-123-R-12, April 1, 2005 - March 31, 2006

Yellow Perch Population Assessment in Southwestern Lake Michigan, Including Evaluation of Sampling Techniques and the Identification Factors that Determine Yellow Perch Year-Class Strength April 1,1997 - March 31, 1998

ID: 8827; issued June 1, 1998

Yellow Perch Population Assessment in Southwestern Lake Michigan, Including Evaluation of Sampling Techniques April 1, 1995 - March 31, 1996

issued June 1996

Yellow Perch Population Assessment in Southwestern Lake Michigan, Including Evaluation of Sampling Techniques and the identification Factors that Determine Yellow Perch Year-Class Strength April 1, 1998 - March 31, 1999

ID: 8863; issued June 1, 1999

Yellow Perch Population Assessment in Southwestern Lake Michigan, Including Evaluation of Sampling Techniques April 1, 1994 - March 31, 1995

issued June 1995

Yellow Perch Population Assessment in Southwestern Lake Michigan, Including Evaluation of Sampling Techniques and Identification of the Factors that Determine Yellow Perch Year-Class Strength April 1, 2007 – March 31, 2008

Division of Fisheries, Illinois Department of Natural Resources Grant/Contract No: F-123 R-14

Yellow perch population assessment in southwestern Lake Michigan, including evaluation of sampling techniques and identification of the factors that determine yellow perch year-class strength April 1, 2006 – March 31, 2007

Division of Fisheries, Illinois Department of Natural Resources Grant/Contract No: F-123 R-13

Yellow Perch Population Assessment in Southwestern Lake Michigan, Including Evaluation of Sampling Techniques and Identification of the Factors that Determine Yellow Perch Year-Class Strength April 1, 2008 – June 30, 2009

Division of Fisheries, Illinois Department of Natural Resources Grant/Contract No: Federal Aid Project F-123 R-15

Sampling strategies for characterization of neuropeptides using mass spectrometry and functional implications into cell-cell signaling

In this dissertation, there are developed different analytical strategies to discover and characterize mammalian brain peptides using small amount of tissues. The magnocellular neurons of rat supraoptic nucleus in tissue and cell culture served as the main model to study neuropeptides, in addition to hippocampal neurons and mouse embryonic pituitaries. The neuropeptidomcis studies described here use different extraction methods on tissue or cell culture combined with mass spectrometry (MS) techniques, matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI). These strategies lead to the identification of multiple peptides from the rat/mouse brain in tissue and cell cultures, including novel compounds One of the goals in this dissertation was to optimize sample preparations on samples isolated from well-defined brain regions for mass spectrometric analysis. Here, the neuropeptidomics study of the SON resulted in the identification of 85 peptides, including 20 unique peptides from known prohormones. This study includes mass spectrometric analysis even from individually isolated magnocellular neuroendocrine cells, where vasopressin and several other peptides are detected. At the same time, it was shown that the same approach could be applied to analyze peptides isolated from a similar hypothalamic region, the suprachiasmatic nucleus (SCN). Although there were some overlaps regarding the detection of the peptides in the two brain nuclei, different peptides were detected specific to each nucleus. Among other peptides, provasopressin fragments were specifically detected in the SON while angiotensin I, somatostatin-14, neurokinin B, galanin, and vasoactive-intestinal peptide (VIP) were detected in the SCN only. Lists of peptides were generated from both brain regions for comparison of the peptidome of SON and SCN nuclei. Moving from analysis of magnocellular neurons in tissue to cell culture, the direct peptidomics of the magnocellular and hippocampal neurons led to the detection of 10 peaks that were assigned to previously characterized peptides and 17 peaks that remain unassigned. Peptides from the vasopressin prohormone and secretogranin-2 are attributed to magnocellular neurons, whereas neurokinin A, peptide J, and neurokinin B are attributed to cultured hippocampal neurons. This approach enabled the elucidation of cell-specific prohormone processing and the discovery of cell-cell signaling peptides. The peptides with roles in the development of the pituitary were analyzed using transgenic mice. Hes1 KO is a genetically modified mouse that lives only e18.5 (embryonic days). Anterior pituitaries of Hes1 null mice exhibit hypoplasia due to increased cell death and reduced proliferation and in the intermediate lobe, the cells differentiate abnormally into somatotropes instead of melanotropes. These previous findings demonstrate that Hes1 has multiple roles in pituitary development, cell differentiation, and cell fate. AVP was detected in all samples. Interestingly, somatostatin [92-100] and provasopressin [151-168] were detected in the mutant but not in the wild type or heterozygous pituitaries while somatostatin-14 was detected only in the heterozygous pituitary. In addition, the putative peptide corresponding to m/z 1330.2 and POMC [205-222] are detected in the mutant and heterozygous pituitaries, but not in the wild type. These results indicate that Hes1 influences the processing of different prohormones having possible roles during development and opens new directions for further developmental studies. This research demonstrates the robust capabilities of MS, which ensures the unbiased direct analysis of peptides extracted from complex biological systems and allows addressing important questions to understand cell-cell signaling in the brain.

Development of error correction techniques for nitrate-N load estimation models

Excess nutrient loads carried by streams and rivers are a great concern for environmental resource managers. In agricultural regions, excess loads are transported downstream to receiving water bodies, potentially causing algal blooms, which could lead to numerous ecological problems. To better understand nutrient load transport, and to develop appropriate water management plans, it is important to have accurate estimates of annual nutrient loads. This study used a Monte Carlo sub-sampling method and error-corrected statistical models to estimate annual nitrate-N loads from two watersheds in central Illinois. The performance of three load estimation methods (the seven-parameter log-linear model, the ratio estimator, and the flow-weighted averaging estimator) applied at one-, two-, four-, six-, and eight-week sampling frequencies were compared. Five error correction techniques; the existing composite method, and four new error correction techniques developed in this study; were applied to each combination of sampling frequency and load estimation method. On average, the most accurate error reduction technique, (proportional rectangular) resulted in 15% and 30% more accurate load estimates when compared to the most accurate uncorrected load estimation method (ratio estimator) for the two watersheds. Using error correction methods, it is possible to design more cost-effective monitoring plans by achieving the same load estimation accuracy with fewer observations. Finally, the optimum combinations of monitoring threshold and sampling frequency that minimizes the number of samples required to achieve specified levels of accuracy in load estimation were determined. For one- to three-weeks sampling frequencies, combined threshold/fixed-interval monitoring approaches produced the best outcomes, while fixed-interval-only approaches produced the most accurate results for four- to eight-weeks sampling frequencies.

Low-power high-performance SAR ADC design with digital calibration techniques

This dissertation presents the design of three high-performance successive-approximation-register (SAR) analog-to-digital converters (ADCs) using distinct digital background calibration techniques under the framework of a generalized code-domain linear equalizer. These digital calibration techniques effectively and efficiently remove the static mismatch errors in the analog-to-digital (A/D) conversion. They enable aggressive scaling of the capacitive digital-to-analog converter (DAC), which also serves as sampling capacitor, to the kT/C limit. As a result, outstanding conversion linearity, high signal-to-noise ratio (SNR), high conversion speed, robustness, superb energy efficiency, and minimal chip-area are accomplished simultaneously. The first design is a 12-bit 22.5/45-MS/s SAR ADC in 0.13-μm CMOS process. It employs a perturbation-based calibration based on the superposition property of linear systems to digitally correct the capacitor mismatch error in the weighted DAC. With 3.0-mW power dissipation at a 1.2-V power supply and a 22.5-MS/s sample rate, it achieves a 71.1-dB signal-to-noise-plus-distortion ratio (SNDR), and a 94.6-dB spurious free dynamic range (SFDR). At Nyquist frequency, the conversion figure of merit (FoM) is 50.8 fJ/conversion step, the best FoM up to date (2010) for 12-bit ADCs. The SAR ADC core occupies 0.06 mm2, while the estimated area the calibration circuits is 0.03 mm2. The second proposed digital calibration technique is a bit-wise-correlation-based digital calibration. It utilizes the statistical independence of an injected pseudo-random signal and the input signal to correct the DAC mismatch in SAR ADCs. This idea is experimentally verified in a 12-bit 37-MS/s SAR ADC fabricated in 65-nm CMOS implemented by Pingli Huang. This prototype chip achieves a 70.23-dB peak SNDR and an 81.02-dB peak SFDR, while occupying 0.12-mm2 silicon area and dissipating 9.14 mW from a 1.2-V supply with the synthesized digital calibration circuits included. The third work is an 8-bit, 600-MS/s, 10-way time-interleaved SAR ADC array fabricated in 0.13-μm CMOS process. This work employs an adaptive digital equalization approach to calibrate both intra-channel nonlinearities and inter-channel mismatch errors. The prototype chip achieves 47.4-dB SNDR, 63.6-dB SFDR, less than 0.30-LSB differential nonlinearity (DNL), and less than 0.23-LSB integral nonlinearity (INL). The ADC array occupies an active area of 1.35 mm2 and dissipates 30.3 mW, including synthesized digital calibration circuits and an on-chip dual-loop delay-locked loop (DLL) for clock generation and synchronization.