Trend analysis in two standard growth models

This paper analyzes the trend processes characterized by two standard growth models using simple econometrics. The first model is the basic neoclassical growth model that postulates a deterministic trend for output. The second model is the Uzawa-Lucas model that postulates a stochastic trend for output. The aim is to understand how the different trend processes for output assumed by these two standard growth models determine the ability of each model to explain the observed trend processes of other macroeconomic variables such as consumption and investment. The results show that the two models reproduce the output trend process. Moreover, the results show that the basic growth model captures properly the consumption trend process, but fails in characterizing the investment trend process. The reverse is true for the Uzawa-Lucas model.

Failure detectors and communication efficiency in the crash and general omisión failure models

122 p.

Preliminary studies on the effects of light duration on the growth and performance of Clarias gariepinus fingerlings

The effects of light duration on the growth and performance of Clarias gariepinus fingerlings were investigated using artificial methods to simulate continuous day length and absolute darkness. The normal day length (12-H Light and 12-H Darkness) served as the control. Among some of the factors affected by the varying photoperiods there were body coloration, feeding efficiency, survival rate and Specific Growth Rate (SGR). There was notably no significant difference between the SGR of the 0-photoperiod culture and the control (P>0.05) but there was significant difference between the 0-photoperiod and the 24-H photoperiod experiment (P<0.05). The haematological profile analysed showed various degrees of changes in the blood parameters of fish cultured under different photoperiods. These changes however, did not show significant differences when subjected to statistical analysis

Latent variable models for neural data analysis

The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 10^11 neurons, each making an average of 10^3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis.

It is divided into two parts. The first begins with an exposition of the general techniques of latent variable modeling. A new, extremely general, optimization algorithm is proposed - called Relaxation Expectation Maximization (REM) - that may be used to learn the optimal parameter values of arbitrary latent variable models. This algorithm appears to alleviate the common problem of convergence to local, sub-optimal, likelihood maxima. REM leads to a natural framework for model size selection; in combination with standard model selection techniques the quality of fits may be further improved, while the appropriate model size is automatically and efficiently determined. Next, a new latent variable model, the mixture of sparse hidden Markov models, is introduced, and approximate inference and learning algorithms are derived for it. This model is applied in the second part of the thesis.

The second part brings the technology of part I to bear on two important problems in experimental neuroscience. The first is known as spike sorting; this is the problem of separating the spikes from different neurons embedded within an extracellular recording. The dissertation offers the first thorough statistical analysis of this problem, which then yields the first powerful probabilistic solution. The second problem addressed is that of characterizing the distribution of spike trains recorded from the same neuron under identical experimental conditions. A latent variable model is proposed. Inference and learning in this model leads to new principled algorithms for smoothing and clustering of spike data.

A via L-arginina-óxido nítrico no transtorno depressivo e sua associação com a função plaquetária e o estresse oxidativo

O transtorno depressivo (TD) é um fator de risco cardiovascular independente que apresenta elevada morbi-mortalidade. Recentes evidências sugerem a participação do óxido nítrico (NO), potente vasodilatador e anti-agregante plaquetário, na patogênese de doenças cardiovasculares e psiquiátricas. A síntese do NO ocorre através da conversão do aminoácido L-arginina em L-citrulina e NO, pela ação da enzima NO sintase (NOS). Esta tese aborda o papel da via L-arginina-NO em plaquetas de pacientes com TD e sua associação com a função plaquetária e estresse oxidativo. Para análise comportamental da depressão em modelo animal, foi utilizado o modelo de estresse pós-natal de separação única (SMU). Os animais foram divididos em quatro grupos para a realização do estudo: Grupo Controle Sedentário (GCS), Grupo Controle Exercício (GCE), Grupo SMU Sedentário (SMUS) e Grupo SMU Exercício (SMUE). O treinamento físico (TF) dos animais englobou 8 semanas, com duração de 30 minutos e uma velocidade de treinamento estabelecida pelo teste máximo (TE). Para o estudo em humanos, 10 pacientes com TD com score Hamilton: 201, (média de idade: 384anos), foram pareados com 10 indivíduos saudáveis (média de idade: 383anos). Os estudos em humanos e animais foram aprovados pelos Comitês de Ética: 1436 - CEP/HUPE e CEUA/047/2010, respectivamente. Foi mensurado em humanos e em animais: transporte de L-arginina, concentração GMPc, atividade das enzimas NOS e superóxido dismutase (SOD) em plaquetas e cortisol sistêmico. Experimentos realizados somente em humanos: expressão das enzimas NOS, arginase e guanilato ciclase através de Western Blotting. A agregação plaquetária foi induzida por colágeno e foi realizada análise sistêmica de proteína C-reativa, fibrinogênio e L-arginina. Para o tratamento estatístico utilizou-se três testes estatísticos para avaliar as diferenças das curvas de sobrevida: Kaplan-Meier, e os testes de Tarone-Ware e Peto-Prentice. Em humanos, houve uma redução do transporte de L-arginina, da atividade das enzimas NOS e SOD, e da concentração de GMPc em plaquetas, e nas concentrações plasmáticas de L-arginina no grupo com TD em relação ao grupo controle. Foi observado um aumento dos níveis plasmáticos de fibrinogênio no TD. Esses resultados demonstram uma inibição da via L-arginina-NO-GMPc e da enzima anti-oxidante SOD em pacientes com TD sem afetar a função plaquetária. Em relação ao TF, para o modelo animal, foram encontradas alterações iniciais quanto à distância percorrida e tempo de execução do TE entre os grupos controles e o grupos SMUs, apresentando estes últimos menores valores para o TE. Após 8 semanas de TF, verificou-se um maior influxo no transporte de L-arginina para o SMUE em comparação ao grupo SMUS. As diferenças observadas para o tempo e a distância percorrida no TE inicial entre os grupos controle e no modelo de estresse foram revertidas após as 8 semanas de TF, demonstrando o efeito benéfico do exercício físico na capacidade cardiorespiratória em modelos de depressão.

Technology for Single Cell Protein Analysis in Immunology and Cancer Prognostics

The first chapter of this thesis deals with automating data gathering for single cell microfluidic tests. The programs developed saved significant amounts of time with no loss in accuracy. The technology from this chapter was applied to experiments in both Chapters 4 and 5.

The second chapter describes the use of statistical learning to prognose if an anti-angiogenic drug (Bevacizumab) would successfully treat a glioblastoma multiforme tumor. This was conducted by first measuring protein levels from 92 blood samples using the DNA-encoded antibody library platform. This allowed the measure of 35 different proteins per sample, with comparable sensitivity to ELISA. Two statistical learning models were developed in order to predict whether the treatment would succeed. The first, logistic regression, predicted with 85% accuracy and an AUC of 0.901 using a five protein panel. These five proteins were statistically significant predictors and gave insight into the mechanism behind anti-angiogenic success/failure. The second model, an ensemble model of logistic regression, kNN, and random forest, predicted with a slightly higher accuracy of 87%.

The third chapter details the development of a photocleavable conjugate that multiplexed cell surface detection in microfluidic devices. The method successfully detected streptavidin on coated beads with 92% positive predictive rate. Furthermore, chambers with 0, 1, 2, and 3+ beads were statistically distinguishable. The method was then used to detect CD3 on Jurkat T cells, yielding a positive predictive rate of 49% and false positive rate of 0%.

The fourth chapter talks about the use of measuring T cell polyfunctionality in order to predict whether a patient will succeed an adoptive T cells transfer therapy. In 15 patients, we measured 10 proteins from individual T cells (~300 cells per patient). The polyfunctional strength index was calculated, which was then correlated with the patient's progress free survival (PFS) time. 52 other parameters measured in the single cell test were correlated with the PFS. No statistical correlator has been determined, however, and more data is necessary to reach a conclusion.

Finally, the fifth chapter talks about the interactions between T cells and how that affects their protein secretion. It was observed that T cells in direct contact selectively enhance their protein secretion, in some cases by over 5 fold. This occurred for Granzyme B, Perforin, CCL4, TNFa, and IFNg. IL- 10 was shown to decrease slightly upon contact. This phenomenon held true for T cells from all patients tested (n=8). Using single cell data, the theoretical protein secretion frequency was calculated for two cells and then compared to the observed rate of secretion for both two cells not in contact, and two cells in contact. In over 90% of cases, the theoretical protein secretion rate matched that of two cells not in contact.

On some new mathematical models for infective diseases: analysis, equilibrium, positivity and vaccination controls

196 p.

Life-stage recruitment models for Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) on Georges Bank

Ichthyoplankton surveys have been used to provide an independent estimate of adult spawning biomass of commercially exploited species and to further our understanding of the recruitment processes in the early life stages. However, predicting recruitment has been difficult because of the complex interaction of physical and biological processes operating at different spatial and temporal scales that can occur at the different life stages. A model of first-year life-stage recruitment was applied to Georges Bank Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) stocks over the years 1977–2004 by using environmental and densitydependent relationships. The best lifestage mortality relationships for eggs, larvae, pelagic juveniles, and demersal juveniles were first determined by hindcasting recruitment estimates based on egg and larval abundance and mortality rates derived from two intensive sampling periods, 1977–87 and 1995–99. A wind-driven egg mortality relationship was used to estimate losses due to transport off the bank, and a wind-stress larval mortality relationship was derived from feeding and survival studies. A simple metric for the density-dependent effects of Atlantic cod was used for both Atlantic cod and haddock. These life stage proxies were then applied to the virtual population analysis (VPA) derived annual egg abundances to predict age-1 recruitment. Best models were determined from the correlation of predicted and VPA-derived age-1 abundance. The larval stage was the most quantifiable of any stage from surveys, whereas abundance estimates of the demersal juvenile stage were not available because of undersampling. Attempts to forecast recruitment from spawning stock biomass or egg abundance, however, will always be poor because of variable egg survival.

The effects of temperature on hatching and survival of northern rock sole larvae (Lepidopsetta polyxystra)

Northern rock sole (Lepidopsetta polyxystra) is a commercially important flatfish in Alaska and was recently classified as a distinct species from southern rock sole (L. bilineata). Taxonomic and vital rate data for northern rock sole are still not fully described, notably at early egg and larval stages. In this study, we provide new taxonomic descriptions of late-stage eggs and newly hatched larvae, as well as temperature-response models of hatching (timing, duration, success), and larval size-at-hatch and posthatch survival at four temperatures (2°, 5°, 9°, and 12°C). Time-to-first-hatch, hatch cycle duration, and overall hatching success showed a negative relationship with temperature. Early hatching larvae within each temperature treatment were smaller and had larger yolk sacs, but larvae incubated at higher temperatures (9° and 12°C) had the largest yolk reserves overall. Despite having smaller yolks, size-at-hatch and the maximum size achieved during the hatching cycle was highest for larvae reared at cold temperatures (2° and 5°C), indicating that endogenous reserves are more efficiently used for growth at these temperatures. In addition, larvae reared at high temperatures died more rapidly in the absence of food despite having more yolk reserves than cold-incubated larvae. Overall, northern rock sole eggs and larvae display early life history traits consistent with coldwater adaptation for winter spawning in the North Pacific.

Asymmetric conservation benefits of circle hooks in multispecies billfish recreational fisheries: a synthesis of hook performance and analysis of blue marlin (Makaira nigricans) postrelease survival

We evaluated the conservation benefits of the use of circle hooks compared with standard J hooks in the recreational fishery for Atlantic istiophorid billfishes, noting hooking location and the presence of trauma (bleeding) for 123 blue marlin (Makaira nigricans), 272 white marlin (Kajikia albida), and 132 sailfish (Istiophorus platypterus) caught on natural baits rigged with one of the two hook types. In addition, we used pop-up satellite archival tags (PSATs) to follow the fate of 61 blue marlin caught on natural baits rigged with circle hooks or on a combination of artificial lure and natural bait rigged with J hooks. The frequencies of internal hooking locations and bleeding were significantly lower with circle hooks than with J hooks for each of the three species and were significantly reduced for blue marlin caught on J hooks than for white marlin and sailfish taken on the same hook type. Analysis of the data received from 59 PSATs (two tags released prematurely) indicated no mortalities among the 29 blue marlin caught on circle hooks and two mortalities among the 30 blue marlin caught on J hooks (6.7%). Collectively, the hook location and PSAT data revealed that blue marlin, like white marlin and sailfish, derive substantial conservation benefits from the use of circle hooks, and the negative impacts of J hooks are significantly reduced for blue marlin relative to the other two species.

Age and growth of spiny dogfish (Squalus acanthias) in the Gulf of Alaska: analysis of alternative growth models

Ten growth models were fitted to age and growth data for spiny dogfish (Squalus acanthias) in the Gulf of Alaska. Previous studies of spiny dogfish growth have all fitted the t0 formulation of the von Bertalanffy model without examination of alternative models. Among the alternatives, we present a new two-phase von Bertalanffy growth model formulation with a logistically scaled k parameter and which estimates L0. A total of 1602 dogfish were aged from opportunistic collections with longline, rod and reel, set net, and trawling gear in the eastern and central Gulf of Alaska between 2004 and 2007. Ages were estimated from the median band count of three independent readings of the second dorsal spine plus the estimated number of worn bands for worn spines. Owing to a lack of small dogfish in the samples, lengths at age of small individuals were back-calculated from a subsample of 153 dogfish with unworn spines. The von Bertalanffy, two-parameter von Bertalanffy, two-phase von Bertalanffy, Gompertz, two-parameter Gompertz, and logistic models were fitted to length-at-age data for each sex separately, both with and without back-calculated lengths at age. The two-phase von Bertalanffy growth model produced the statistically best fit for both sexes of Gulf of Alaska spiny dogfish, resulting in L∞ = 87.2 and 102.5 cm and k= 0.106 and 0.058 for males and females, respectively.