A Standardized Ultrasonography Classification for Channel Catfish Ovarian Development

The goal of this dissertation was to develop application of ultrasonography as a decision-making tool in genetic improvement programs for channel catfish Ictalurus punctatus. A literature review on the use of ultrasonography in fish reproduction generated a comprehensive reference data set intended to benefit existing and potential users. It exposed the need for reporting of instrument control settings and standardization of fish handling and imaging procedures. These issues were addressed from the onset of this work by assessing more than 6,300 channel catfish ovaries by use of initial fish handling and imaging procedures developed (2004-2005) at the Louisiana State University Agricultural Center Aquaculture Research Station. The development of a standardized and systematic approach to interpretation of ultrasound images emphasized the interplay of technical and biological aspects of ultrasonography assessments. This showed the importance of the control settings and identified disruptive ultrasound artifacts to avoid for observation of the ovary and oocytes. A preliminary ultrasound imaging classification index for assessing ovarian development during the annual reproductive cycle was developed, used and evaluated. This led to the creation of seven well-defined, standardized ultrasound imaging classifications of channel catfish ovarian development based on the annual cycle. Histology of each ultrasound image in the classification index was included as a Reference Guide to provide insight into the processes observed during ultrasonography. Finally, the ultrasound imaging classification index was used for identification and selection of females for hormone-induced spawning in commercial hatchery production of F1 hybrids (channel catfish female x blue catfish male I. furcatus). In sum, this dissertation provides a systematic method of ultrasound imaging assessment of channel catfish ovarian development enabling progress towards standardization in the use of ultrasonography in fish reproduction

Surface Morphology and Electrical Resistivity in Polycrystalline Au/Cu/Si(100) System

This work describes the analysis of morphology and electrical resistivity (ρ) obtained in the Au/Cu/Si system. The Au/Cu bilayers were deposited by thermal evaporation technique with thicknesses from 50 to 250 nm on SiOx/Si(100) substrates. The Au : Cu concentration ratio of the samples was of 25 : 75 at%. The bilayers were annealed into a vacuum oven with argon atmosphere at 660 K for one hour. The crystalline structures of AuCu and CuSi alloys were confirmed by X-ray diffraction analysis. The scanning electron microscopy (SEM), the atomic force microscopy (AFM), and the energy dispersive spectroscopy (EDS) were used to study the morphology, final thickness, and the atomic concentration of the alloys formed, respectively. The four-point probe technique was used to measure the electrical resistivity (ρ) in the prepared alloys as a function of thickness. The ρ value was measured and it was numerically compared with the Fuchs–Sondheimer (FS) and the Mayadas–Shatzkes (MS) models of resistivity. Results show values of electrical resistivity between 0.9 and 1.9 μΩ-cm. These values are four times smaller than the values of the AuCu systems reported in literature

Baseline Characteristics of Sars-Cov-2 Vaccine Non-Responders in a Large Population-Based Sample

INTRODUCTION: Studies indicate that individuals with chronic conditions and specific baseline characteristics may not mount a robust humoral antibody response to SARS-CoV-2 vaccines. In this paper, we used data from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a longitudinal state-wide seroprevalence program that has enrolled more than 90,000 participants, to evaluate the role of chronic diseases as the potential risk factors of non-response to SARS-CoV-2 vaccines in a large epidemiologic cohort. METHODS: A participant needed to complete an online survey and a blood draw to test for SARS-CoV-2 circulating plasma antibodies at four-time points spaced at least three months apart. Chronic disease predictors of vaccine non-response are evaluated using logistic regression with non-response as the outcome and each chronic disease + age as the predictors. RESULTS: As of April 24, 2023, 18,240 participants met the inclusion criteria; 0.58% (N = 105) of these are non-responders. Adjusting for age, our results show that participants with self-reported immunocompromised status, kidney disease, cancer, and other non-specified comorbidity were 15.43, 5.11, 2.59, and 3.13 times more likely to fail to mount a complete response to a vaccine, respectively. Furthermore, having two or more chronic diseases doubled the prevalence of non-response. CONCLUSION: Consistent with smaller targeted studies, a large epidemiologic cohort bears the same conclusion and demonstrates immunocompromised, cancer, kidney disease, and the number of diseases are associated with vaccine non-response. This study suggests that those individuals, with chronic diseases with the potential to affect their immune system response, may need increased doses or repeated doses of COVID-19 vaccines to develop a protective antibody level

Methodology to Estimate Natural- and Vaccine-induced antibodies to Sars-Cov-2 in a Large Geographic Region

Accurate estimates of natural and/or vaccine-induced antibodies to SARS-CoV-2 are difficult to obtain. Although model-based estimates of seroprevalence have been proposed, they require inputting unknown parameters including viral reproduction number, longevity of immune response, and other dynamic factors. In contrast to a model-based approach, the current study presents a data-driven detailed statistical procedure for estimating total seroprevalence (defined as antibodies from natural infection or from full vaccination) in a region using prospectively collected serological data and state-level vaccination data. Specifically, we conducted a longitudinal statewide serological survey with 88,605 participants 5 years or older with 3 prospective blood draws beginning September 30, 2020. Along with state vaccination data, as of October 31, 2021, the estimated percentage of those 5 years or older with naturally occurring antibodies to SARS-CoV-2 in Texas is 35.0% (95% CI = (33.1%, 36.9%)). This is 3× higher than, state-confirmed COVID-19 cases (11.83%) for all ages. The percentage with naturally occurring or vaccine-induced antibodies (total seroprevalence) is 77.42%. This methodology is integral to pandemic preparedness as accurate estimates of seroprevalence can inform policy-making decisions relevant to SARS-CoV-2

Antibody Duration after infection From Sars-Cov-2 in the Texas Coronavirus antibody Response Survey

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not)