Actas capitulares de Catamarca, 23 sept. 1809 a 31 de dic. 1814: copiadas literalmente.

Synopsis, introd.

Dic Sion Dayfdd. Richard Jones-Davies, Esquire.

Mode of access: Internet.

Dicţionarul statistic al României intocmit pe baza rezultatelor definitive ale recensământului general al populaţiunii din 19 Decembre 1912. Ed. oficială.

I. Argeş-Iaşi.--II. Ilfov-Viaşca.

Zur kenntnis der acetylierung und carbanilidbildung bei 1,3-dic...

Thesis (doctoral)--

Effect of Inorganic and Organic Carbon Enrichments (DIC and DOC) on the Photosynthesis and Calcification Rates of Two Calcifying Green Algae from a Caribbean Reef Lagoon

Coral reefs worldwide are affected by increasing dissolved inorganic carbon (DIC) and organic carbon (DOC) concentrations due to ocean acidification (OA) and coastal eutrophication. These two stressors can occur simultaneously, particularly in near-shore reef environments with increasing anthropogenic pressure. However, experimental studies on how elevated DIC and DOC interact are scarce and fundamental to understanding potential synergistic effects and foreseeing future changes in coral reef function. Using an open mesocosm experiment, the present study investigated the impact of elevated DIC (pHNBS: 8.2 and 7.8; pCO2: 377 and 1076 μatm) and DOC (added as 833 μmol L-1 of glucose) on calcification and photosynthesis rates of two common calcifying green algae, Halimeda incrassata and Udotea flabellum, in a shallow reef environment. Our results revealed that under elevated DIC, algal photosynthesis decreased similarly for both species, but calcification was more affected in H. incrassata, which also showed carbonate dissolution rates. Elevated DOC reduced photosynthesis and calcification rates in H. incrassata, while in U. flabellum photosynthesis was unaffected and thalus calcification was severely impaired. The combined treatment showed an antagonistic effect of elevated DIC and DOC on the photosynthesis and calcification rates of H. incrassata, and an additive effect in U. flabellum. We conclude that the dominant sand dweller H. incrassata is more negatively affected by both DIC and DOC enrichments, but that their impact could be mitigated when they occur simultaneously. In contrast, U. flabellum can be less affected in coastal eutrophic waters by elevated DIC, but its contribution to reef carbonate sediment production could be further reduced. Accordingly, while the capacity of environmental eutrophication to exacerbate the impact of OA on algal-derived carbonate sand production seems to be species-specific, significant reductions can be expected under future OA scenarios, with important consequences for beach erosion and coastal sediment dynamics.

Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology

1. Ecological data sets often use clustered measurements or use repeated sampling in a longitudinal design. Choosing the correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated observations. 2. Three methods for choosing the covariance are: the Akaike information criterion (AIC), the quasi-information criterion (QIC), and the deviance information criterion (DIC). We compared the methods using a simulation study and using a data set that explored effects of forest fragmentation on avian species richness over 15 years. 3. The overall success was 80.6% for the AIC, 29.4% for the QIC and 81.6% for the DIC. For the forest fragmentation study the AIC and DIC selected the unstructured covariance, whereas the QIC selected the simpler autoregressive covariance. Graphical diagnostics suggested that the unstructured covariance was probably correct. 4. We recommend using DIC for selecting the correct covariance structure.

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.

On the nature of over-dispersion in motor vehicle crash prediction models

Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts—variation over and above that accounted for by the Poisson density. The extra-variation – or dispersion – is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models—tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31–40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions. This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with non-informative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites

Biaxial cell stimulation : a mechanical validation

To analyse mechanotransduction resulting from tensile loading under defined conditions, various devices for in vitro cell stimulation have been developed. This work aimed to determine the strain distribution on the membrane of a commercially available device and its consistency with rising cycle numbers, as well as the amount of strain transferred to adherent cells. The strains and their behaviour within the stimulation device were determined using digital image correlation (DIC). The strain transferred to cells was measured on eGFP-transfected bone marrow-derived cells imaged with a fluorescence microscope. The analysis was performed by determining the coordinates of prominent positions on the cells, calculating vectors between the coordinates and their length changes with increasing applied tensile strain. The stimulation device was found to apply homogeneous (mean of standard deviations approx. 2% of mean strain) and reproducible strains in the central well area. However, on average, only half of the applied strain was transferred to the bone marrow-derived cells. Furthermore, the strain measured within the device increased significantly with an increasing number of cycles while the membrane's Young's modulus decreased, indicating permanent changes in the material during extended use. Thus, strain magnitudes do not match the system readout and results require careful interpretation, especially at high cycle numbers.

Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis

Most crash severity studies ignored severity correlations between driver-vehicle units involved in the same crashes. Models without accounting for these within-crash correlations will result in biased estimates in the factor effects. This study developed a Bayesian hierarchical binomial logistic model to identify the significant factors affecting the severity level of driver injury and vehicle damage in traffic crashes at signalized intersections. Crash data in Singapore were employed to calibrate the model. Model fitness assessment and comparison using Intra-class Correlation Coefficient (ICC) and Deviance Information Criterion (DIC) ensured the suitability of introducing the crash-level random effects. Crashes occurring in peak time, in good street lighting condition, involving pedestrian injuries are associated with a lower severity, while those in night time, at T/Y type intersections, on right-most lane, and installed with red light camera have larger odds of being severe. Moreover, heavy vehicles have a better resistance on severe crash, while crashes involving two-wheel vehicles, young or aged drivers, and the involvement of offending party are more likely to result in severe injuries.

Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation

Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast real-time MUNE method has proved elusive hitherto. Ridall et al. (2007) implement a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to produce a posterior distribution for the number of motor units using a Bayesian hierarchical model that takes into account biological information about motor unit activation. However we find that the approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. Here we focus on improved inference by marginalising over latent variables to create the likelihood. In particular we explore how this can improve the RJMCMC mixing and investigate alternative approaches that utilise the likelihood (e.g. DIC (Spiegelhalter et al., 2002)). For this model the marginalisation is over latent variables which, for a larger number of motor units, is an intractable summation over all combinations of a set of latent binary variables whose joint sample space increases exponentially with the number of motor units. We provide a tractable and accurate approximation for this quantity and also investigate simulation approaches incorporated into RJMCMC using results of Andrieu and Roberts (2009).

Establishing prostate cancer patient derived xenografts: Lessons learned from older studies

Background Understanding the progression of prostate cancer to androgen-independence/castrate resistance and development of preclinical testing models are important for developing new prostate cancer therapies. This report describes studies performed 30 years ago, which demonstrate utility and shortfalls of xenografting to preclinical modeling. Methods We subcutaneously implanted male nude mice with small prostate cancer fragments from transurethral resection of the prostate (TURP) from 29 patients. Successful xenografts were passaged into new host mice. They were characterized using histology, immunohistochemistry for marker expression, flow cytometry for ploidy status, and in some cases by electron microscopy and response to testosterone. Two xenografts were karyotyped by G-banding. Results Tissues from 3/29 donors (10%) gave rise to xenografts that were successfully serially passaged in vivo. Two, (UCRU-PR-1, which subsequently was replaced by a mouse fibrosarcoma, and UCRU-PR-2, which combined epithelial and neuroendocrine features) have been described. UCRU-PR-4 line was a poorly differentiated prostatic adenocarcinoma derived from a patient who had undergone estrogen therapy and bilateral castration after his cancer relapsed. Histologically, this comprised diffusely infiltrating small acinar cell carcinoma with more solid aggregates of poorly differentiated adenocarcinoma. The xenografted line showed histology consistent with a poorly differentiated adenocarcinoma and stained positively for prostatic acid phosphatase (PAcP), epithelial membrane antigen (EMA) and the cytokeratin cocktail, CAM5.2, with weak staining for prostate specific antigen (PSA). The line failed to grow in female nude mice. Castration of three male nude mice after xenograft establishment resulted in cessation of growth in one, growth regression in another and transient growth in another, suggesting that some cells had retained androgen sensitivity. The karyotype (from passage 1) was 43–46, XY, dic(1;12)(p11;p11), der(3)t(3:?5)(q13;q13), -5, inv(7)(p15q35) x2, +add(7)(p13), add(8)(p22), add(11)(p14), add(13)(p11), add(20)(p12), -22, +r4[cp8]. Conclusions Xenografts provide a clinically relevant model of prostate cancer, although establishing serially transplantable prostate cancer patient derived xenografts is challenging and requires rigorous characterization and high quality starting material. Xenografting from advanced prostate cancer is more likely to succeed, as xenografting from well differentiated, localized disease has not been achieved in our experience. Strong translational correlations can be demonstrated between the clinical disease state and the xenograft model